diff --git a/.gitignore b/.gitignore index 91aa94e85..7df232a6f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,10 +1,197 @@ +### C++ ### +# Prerequisites +*.d + +# Compiled Object files +*.slo +*.lo +*.o +*.obj + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod +*.smod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app + +### Linux ### +*~ + +# temporary files which can be created if a process still has a handle open of a deleted file +.fuse_hidden* + +# KDE directory preferences +.directory + +# Linux trash folder which might appear on any partition or disk +.Trash-* + +# .nfs files are created when an open file is removed but is still being accessed +.nfs* + +### macOS ### +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +### PyCharm ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# AWS User-specific +.idea/**/aws.xml + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. 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If the Library as you +received it does not specify a version number of the GNU Lesser +General Public License, you may choose any version of the GNU Lesser +General Public License ever published by the Free Software Foundation. + + If the Library as you received it specifies that a proxy can decide +whether future versions of the GNU Lesser General Public License shall +apply, that proxy's public statement of acceptance of any version is +permanent authorization for you to choose that version for the +Library. diff --git a/dmff/__init__.py b/dmff/__init__.py index e69de29bb..19eacb97d 100644 --- a/dmff/__init__.py +++ b/dmff/__init__.py @@ -0,0 +1 @@ +import dmff.settings \ No newline at end of file diff --git a/dmff/admp/disp_pme.py b/dmff/admp/disp_pme.py new file mode 100755 index 000000000..3df7d11d6 --- /dev/null +++ b/dmff/admp/disp_pme.py @@ -0,0 +1,353 @@ +import jax.numpy as jnp +from jax import vmap, value_and_grad +from dmff.utils import jit_condition +from dmff.admp.spatial import pbc_shift +from dmff.admp.pme import setup_ewald_parameters +from dmff.admp.recip import generate_pme_recip, Ck_6, Ck_8, Ck_10 +from dmff.admp.pairwise import distribute_scalar, distribute_v3, distribute_dispcoeff +from functools import partial + +class ADMPDispPmeForce: + ''' + This is a convenient wrapper for dispersion PME calculations + It wrapps all the environment parameters of multipolar PME calculation + The so called "environment paramters" means parameters that do not need to be differentiable + ''' + + def __init__(self, box, covalent_map, rc, ethresh, pmax): + self.covalent_map = covalent_map + self.rc = rc + self.ethresh = ethresh + self.pmax = pmax + # Need a different function for dispersion ??? Need tests + kappa, K1, K2, K3 = setup_ewald_parameters(rc, ethresh, box) + self.kappa = kappa + self.K1 = K1 + self.K2 = K2 + self.K3 = K3 + self.pme_order = 6 + # setup calculators + self.refresh_calculators() + return + + + def generate_get_energy(self): + def get_energy(positions, box, pairs, c_list, mScales): + return energy_disp_pme(positions, box, pairs, + c_list, mScales, self.covalent_map, + self.kappa, self.K1, self.K2, self.K3, self.pmax, + self.d6_recip, self.d8_recip, self.d10_recip) + return get_energy + + + def update_env(self, attr, val): + ''' + Update the environment of the calculator + ''' + setattr(self, attr, val) + self.refresh_calculators() + + + def refresh_calculators(self): + ''' + refresh the energy and force calculator according to the current environment + ''' + self.d6_recip = generate_pme_recip(Ck_6, self.kappa, True, self.pme_order, self.K1, self.K2, self.K3, 0) + if self.pmax >= 8: + self.d8_recip = generate_pme_recip(Ck_8, self.kappa, True, self.pme_order, self.K1, self.K2, self.K3, 0) + else: + self.d8_recip = None + if self.pmax >= 10: + self.d10_recip = generate_pme_recip(Ck_10, self.kappa, True, self.pme_order, self.K1, self.K2, self.K3, 0) + else: + self.d10_recip = None + # create the energy calculator according to PME environment + self.get_energy = self.generate_get_energy() + self.get_forces = value_and_grad(self.get_energy) + return + + +def energy_disp_pme(positions, box, pairs, + c_list, mScales, covalent_map, + kappa, K1, K2, K3, pmax, + recip_fn6, recip_fn8, recip_fn10): + ''' + Top level wrapper for dispersion pme + + Input: + positions: + Na * 3: positions + box: + 3 * 3: box, axes arranged in row + pairs: + Np * 2: interacting pair indices + c_list: + Na * (pmax-4)/2: atomic dispersion coefficients + mScales: + (Nexcl,): permanent multipole-multipole interaction exclusion scalings: 1-2, 1-3 ... + covalent_map: + Na * Na: topological distances between atoms, if i, j are topologically distant, then covalent_map[i, j] == 0 + disp_pme_recip_fn: + function: the reciprocal calculator, see recip.py + kappa: + float: kappa in A^-1 + K1, K2, K3: + int: max K for reciprocal calculations + pmax: + int array: maximal exponents (p) to compute, e.g., (6, 8, 10) + + Output: + energy: total dispersion pme energy + ''' + + ene_real = disp_pme_real(positions, box, pairs, c_list, mScales, covalent_map, kappa, pmax) + + ene_recip = recip_fn6(positions, box, c_list[:, 0, jnp.newaxis]) + if pmax >= 8: + ene_recip += recip_fn8(positions, box, c_list[:, 1, jnp.newaxis]) + if pmax >= 10: + ene_recip += recip_fn10(positions, box, c_list[:, 2, jnp.newaxis]) + + ene_self = disp_pme_self(c_list, kappa, pmax) + + return ene_real + ene_recip + ene_self + + +def disp_pme_real(positions, box, pairs, + c_list, + mScales, covalent_map, + kappa, pmax): + ''' + This function calculates the dispersion real space energy + It expands the atomic parameters to pairwise parameters + + Input: + positions: + Na * 3: positions + box: + 3 * 3: box, axes arranged in row + pairs: + Np * 2: interacting pair indices + c_list: + Na * (pmax-4)/2: atomic dispersion coefficients + mScales: + (Nexcl,): permanent multipole-multipole interaction exclusion scalings: 1-2, 1-3 ... + covalent_map: + Na * Na: topological distances between atoms, if i, j are topologically distant, then covalent_map[i, j] == 0 + kappa: + float: kappa in A^-1 + pmax: + int array: maximal exponents (p) to compute, e.g., (6, 8, 10) + + Output: + ene: dispersion pme realspace energy + ''' + + # expand pairwise parameters + pairs = pairs[pairs[:, 0] < pairs[:, 1]] + + box_inv = jnp.linalg.inv(box) + + ri = distribute_v3(positions, pairs[:, 0]) + rj = distribute_v3(positions, pairs[:, 1]) + # ri = positions[pairs[:, 0]] + # rj = positions[pairs[:, 1]] + nbonds = covalent_map[pairs[:, 0], pairs[:, 1]] + mscales = distribute_scalar(mScales, nbonds-1) + # mscales = mScales[nbonds-1] + + ci = distribute_dispcoeff(c_list, pairs[:, 0]) + cj = distribute_dispcoeff(c_list, pairs[:, 1]) + # ci = c_list[pairs[:, 0], :] + # cj = c_list[pairs[:, 1], :] + + ene_real = jnp.sum(disp_pme_real_kernel(ri, rj, ci, cj, box, box_inv, mscales, kappa, pmax)) + + return jnp.sum(ene_real) + + +@partial(vmap, in_axes=(0, 0, 0, 0, None, None, 0, None, None), out_axes=(0)) +@jit_condition(static_argnums=(8)) +def disp_pme_real_kernel(ri, rj, ci, cj, box, box_inv, mscales, kappa, pmax): + ''' + The kernel to calculate the realspace dispersion energy + + Inputs: + ri: + Np * 3: position i + rj: + Np * 3: position j + ci: + Np * (pmax-4)/2: dispersion coeffs of i, c6, c8, c10 etc + cj: + Np * (pmax-4)/2: dispersion coeffs of j, c6, c8, c10 etc + kappa: + float: kappa + pmax: + int: largest p in 1/r^p, assume starting from 6 with increment of 2 + + Output: + energy: + float: the dispersion pme energy + ''' + dr = ri - rj + dr = pbc_shift(dr, box, box_inv) + dr2 = jnp.dot(dr, dr) + x2 = kappa * kappa * dr2 + g = g_p(x2, pmax) + dr6 = dr2 * dr2 * dr2 + ene = (mscales + g[0] - 1) * ci[0] * cj[0] / dr6 + if pmax >= 8: + dr8 = dr6 * dr2 + ene += (mscales + g[1] - 1) * ci[1] * cj[1] / dr8 + if pmax >= 10: + dr10 = dr8 * dr2 + ene += (mscales + g[2] - 1) * ci[2] * cj[2] / dr10 + return ene + + +def g_p(x2, pmax): + ''' + Compute the g(x, p) function + + Inputs: + x: + float: the input variable + pmax: + int: the maximal powers of dispersion, here we assume evenly spacing even powers starting from 6 + e.g., (6,), (6, 8) or (6, 8, 10) + + Outputs: + g: + (p-4)//2: g(x, p) + ''' + + x4 = x2 * x2 + x8 = x4 * x4 + exp_x2 = jnp.exp(-x2) + g6 = 1 + x2 + 0.5*x4 + if pmax >= 8: + g8 = g6 + x4*x2/6 + if pmax >= 10: + g10 = g8 + x8/24 + + if pmax == 6: + g = jnp.array([g6]) + elif pmax == 8: + g = jnp.array([g6, g8]) + elif pmax == 10: + g = jnp.array([g6, g8, g10]) + + return g * exp_x2 + + +@jit_condition(static_argnums=(2)) +def disp_pme_self(c_list, kappa, pmax): + ''' + This function calculates the dispersion self energy + + Inputs: + c_list: + Na * 3: dispersion susceptibilities C_6, C_8, C_10 + kappa: + float: kappa used in dispersion + + Output: + ene_self: + float: the self energy + ''' + E_6 = -kappa**6/12 * jnp.sum(c_list[:, 0]**2) + if pmax >= 8: + E_8 = -kappa**8/48 * jnp.sum(c_list[:, 1]**2) + if pmax >= 10: + E_10 = -kappa**10/240 * jnp.sum(c_list[:, 2]**2) + E = E_6 + if pmax >= 8: + E += E_8 + if pmax >= 10: + E += E_10 + return E + + +# def validation(pdb): +# xml = 'mpidwater.xml' +# pdbinfo = read_pdb(pdb) +# serials = pdbinfo['serials'] +# names = pdbinfo['names'] +# resNames = pdbinfo['resNames'] +# resSeqs = pdbinfo['resSeqs'] +# positions = pdbinfo['positions'] +# box = pdbinfo['box'] # a, b, c, α, β, γ +# charges = pdbinfo['charges'] +# positions = jnp.asarray(positions) +# lx, ly, lz, _, _, _ = box +# box = jnp.eye(3)*jnp.array([lx, ly, lz]) + +# mScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) +# pScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) +# dScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + +# rc = 4 # in Angstrom +# ethresh = 1e-4 + +# n_atoms = len(serials) + +# atomTemplate, residueTemplate = read_xml(xml) +# atomDicts, residueDicts = init_residues(serials, names, resNames, resSeqs, positions, charges, atomTemplate, residueTemplate) + +# covalent_map = assemble_covalent(residueDicts, n_atoms) +# displacement_fn, shift_fn = space.periodic_general(box, fractional_coordinates=False) +# neighbor_list_fn = partition.neighbor_list(displacement_fn, box, rc, 0, format=partition.OrderedSparse) +# nbr = neighbor_list_fn.allocate(positions) +# pairs = nbr.idx.T + +# pmax = 10 +# kappa, K1, K2, K3 = setup_ewald_parameters(rc, ethresh, box) +# kappa = 0.657065221219616 + +# # construct the C list +# c_list = np.zeros((3,n_atoms)) +# nmol=int(n_atoms/3) +# for i in range(nmol): +# a = i*3 +# b = i*3+1 +# c = i*3+2 +# c_list[0][a]=37.19677405 +# c_list[0][b]=7.6111103 +# c_list[0][c]=7.6111103 +# c_list[1][a]=85.26810658 +# c_list[1][b]=11.90220148 +# c_list[1][c]=11.90220148 +# c_list[2][a]=134.44874488 +# c_list[2][b]=15.05074749 +# c_list[2][c]=15.05074749 +# c_list = jnp.array(c_list.T) + + +# # Finish data preparation +# # ------------------------------------------------------------------------------------- +# # pme_order = 6 +# # d6_recip = generate_pme_recip(Ck_6, kappa, True, pme_order, K1, K2, K3, 0) +# # d8_recip = generate_pme_recip(Ck_8, kappa, True, pme_order, K1, K2, K3, 0) +# # d10_recip = generate_pme_recip(Ck_10, kappa, True, pme_order, K1, K2, K3, 0) +# # disp_pme_recip_fns = [d6_recip, d8_recip, d10_recip] +# # energy_force_disp_pme = value_and_grad(energy_disp_pme) +# # e, f = energy_force_disp_pme(positions, box, pairs, c_list, mScales, covalent_map, kappa, K1, K2, K3, pmax, *disp_pme_recip_fns) +# # print('ok') +# # e, f = energy_force_disp_pme(positions, box, pairs, c_list, mScales, covalent_map, kappa, K1, K2, K3, pmax, *disp_pme_recip_fns) +# # print(e) + +# disp_pme_force = ADMPDispPmeForce(box, covalent_map, rc, ethresh, pmax) +# disp_pme_force.update_env('kappa', 0.657065221219616) + +# print(c_list[:4]) +# E, F = disp_pme_force.get_forces(positions, box, pairs, c_list, mScales) +# print('ok') +# E, F = disp_pme_force.get_forces(positions, box, pairs, c_list, mScales) +# print(E) +# return + + +# # below is the validation code +# if __name__ == '__main__': +# validation(sys.argv[1]) diff --git a/dmff/admp/multipole.py b/dmff/admp/multipole.py new file mode 100644 index 000000000..c894db6fa --- /dev/null +++ b/dmff/admp/multipole.py @@ -0,0 +1,199 @@ +import sys +import jax.numpy as jnp +from jax import vmap +from dmff.utils import jit_condition +from functools import partial + +# This module deals with the transformations and rotations of multipoles + + +# The important conversion matrices used in multipoles +rt3 = 1.73205080757 +inv_rt3 = 1.0/rt3 +# the dipole conversion matrices, cart2harm and harm2cart +C1_h2c = jnp.array([[0, 1, 0], + [0, 0, 1], + [1, 0, 0]]) +C1_c2h = C1_h2c.T +# the quadrupole conversion matrices +C2_c2h = jnp.array([[ 0, 0, 1, 0, 0, 0], + [ 0, 0, 0, 0, 2*inv_rt3, 0], + [ 0, 0, 0, 0, 0, 2*inv_rt3], + [inv_rt3, -inv_rt3, 0, 0, 0, 0], + [ 0, 0, 0, 2*inv_rt3, 0, 0]]) + +C2_h2c = jnp.array([[-0.5, 0, 0, rt3/2, 0], + [-0.5, 0, 0, -rt3/2, 0], + [ 1, 0, 0, 0, 0], + [ 0, 0, 0, 0, rt3/2], + [ 0, rt3/2, 0, 0, 0], + [ 0, 0, rt3/2, 0, 0]]) + + +@partial(vmap, in_axes=(0, None), out_axes=0) +@jit_condition(static_argnums=(1)) +def convert_cart2harm(Theta, lmax): + ''' + Convert the multipole moments in cartesian repr to spherical harmonic repr + Essentially, implementing the equations in Appendix E in Anthony's book + + Inputs: + Theta: + n * N_cart: stores the cartesian multipoles of each site + lmax: + integer, the maximum L, currently only supports up to quadrupole + + Outputs: + Q: + n * (l+1)^2, stores the spherical multipoles + ''' + if lmax > 2: + sys.exit('l > 2 (beyond quadrupole) not supported') + + # n_sites = Theta.shape[0] + n_harm = (lmax + 1)**2 + # Q = jnp.zeros((n_sites, n_harm)) + Q_mono = Theta[0:1] + + # dipole + if lmax >= 1: + dipoles_cart = Theta[1:4].T + Q_dip = C1_c2h.dot(dipoles_cart).T + # quadrupole + if lmax >= 2: + quad_cart = Theta[4:10].T + Q_quad = C2_c2h.dot(quad_cart).T + + if lmax == 0: + Q = Q_mono + elif lmax == 1: + Q = jnp.hstack([Q_mono, Q_dip]) + else: + Q = jnp.hstack([Q_mono, Q_dip, Q_quad]) + + return Q + + +@partial(vmap, in_axes=(0, 0), out_axes=0) +@jit_condition(static_argnums=()) +def rot_ind_global2local(U_g, localframes): + ''' + A special rotation function for just dipoles, aim for applying on induced dipoles + ''' + zxy = jnp.array([2,0,1]) + R1 = localframes[zxy][:,zxy] + U_l = jnp.dot(R1, U_g) + return U_l + + +@partial(vmap, in_axes=(0, 0, None), out_axes=0) +@jit_condition(static_argnums=(2)) +def rot_global2local(Q_gh, localframes, lmax=2): + ''' + This function rotates harmonic moments Q from global frame to local frame + + Input: + Q_gh: + n * (l+1)^2, stores the global harmonic multipole moments of each site + localframes: + n * 3 * 3, stores the Rotation matrix for each site, the R is defined as: + [r1, r2, r3]^T, with r1, r2, r3 being the local frame axes + lmax: + integer, the maximum multipole order + + Output: + Q_lh: + n * (l+1)^2, stores the local harmonic multipole moments + ''' + if lmax > 2: + raise NotImplementedError('l > 2 (beyond quadrupole) not supported') + + # monopole + Q_lh_0 = Q_gh[0:1] + # for dipole + if lmax >= 1: + zxy = jnp.array([2,0,1]) + # the rotation matrix + R1 = localframes[zxy][:,zxy] + # rotate + Q_lh_1 = jnp.dot(R1, Q_gh[1:4]) + if lmax >= 2: + xx = localframes[0, 0] + xy = localframes[0, 1] + xz = localframes[0, 2] + yx = localframes[1, 0] + yy = localframes[1, 1] + yz = localframes[1, 2] + zx = localframes[2, 0] + zy = localframes[2, 1] + zz = localframes[2, 2] + quadrupoles = Q_gh[4:9] + # construct the local->global transformation matrix + # this is copied directly from the convert_mom_to_xml.py code + C2_gl_00 = (3*zz**2-1)/2 + C2_gl_01 = rt3*zx*zz + C2_gl_02 = rt3*zy*zz + C2_gl_03 = (rt3*(-2*zy**2-zz**2+1))/2 + C2_gl_04 = rt3*zx*zy + C2_gl_10 = rt3*xz*zz + C2_gl_11 = 2*xx*zz-yy + C2_gl_12 = yx+2*xy*zz + C2_gl_13 = -2*xy*zy-xz*zz + C2_gl_14 = xx*zy+zx*xy + C2_gl_20 = rt3*yz*zz + C2_gl_21 = 2*yx*zz+xy + C2_gl_22 = -xx+2*yy*zz + C2_gl_23 = -2*yy*zy-yz*zz + C2_gl_24 = yx*zy+zx*yy + C2_gl_30 = rt3*(-2*yz**2-zz**2+1)/2 + C2_gl_31 = -2*yx*yz-zx*zz + C2_gl_32 = -2*yy*yz-zy*zz + C2_gl_33 = (4*yy**2+2*zy**2+2*yz**2+zz**2-3)/2 + C2_gl_34 = -2*yx*yy-zx*zy + C2_gl_40 = rt3*xz*yz + C2_gl_41 = xx*yz+yx*xz + C2_gl_42 = xy*yz+yy*xz + C2_gl_43 = -2*xy*yy-xz*yz + C2_gl_44 = xx*yy+yx*xy + # rotate + C2_gl = jnp.array( + [ + [C2_gl_00, C2_gl_10, C2_gl_20, C2_gl_30, C2_gl_40], + [C2_gl_01, C2_gl_11, C2_gl_21, C2_gl_31, C2_gl_41], + [C2_gl_02, C2_gl_12, C2_gl_22, C2_gl_32, C2_gl_42], + [C2_gl_03, C2_gl_13, C2_gl_23, C2_gl_33, C2_gl_43], + [C2_gl_04, C2_gl_14, C2_gl_24, C2_gl_34, C2_gl_44] + ] + ).swapaxes(0,1) + Q_lh_2 = jnp.einsum('jk,k->j', C2_gl, quadrupoles) + if lmax == 0: + Q_lh = Q_lh_0 + elif lmax == 1: + Q_lh = jnp.hstack([Q_lh_0, Q_lh_1]) + elif lmax == 2: + Q_lh = jnp.hstack([Q_lh_0, Q_lh_1, Q_lh_2]) + + return Q_lh + + +# @jit_condition(static_argnums=(2)) +def rot_local2global(Q_lh, localframes, lmax=2): + ''' + This function rotates harmonic moments Q from global frame to local frame + Simply use the rot_global2local, and localframe^-1 + + Input: + Q_lh: + n * (l+1)^2, stores the local harmonic multipole moments of each site + localframes: + n * 3 * 3, stores the Rotation matrix for each site, the R is defined as: + [r1, r2, r3]^T, with r1, r2, r3 being the local frame axes + lmax: + integer, the maximum multipole order + + Output: + Q_gh: + n * (l+1)^2, stores the rotated global harmonic multipole moments + ''' + return rot_global2local(Q_lh, jnp.swapaxes(localframes, -2, -1), lmax) + diff --git a/dmff/admp/pairwise.py b/dmff/admp/pairwise.py new file mode 100755 index 000000000..a789cc0eb --- /dev/null +++ b/dmff/admp/pairwise.py @@ -0,0 +1,214 @@ +import sys +from jax import vmap +import jax.numpy as jnp +from dmff.utils import jit_condition +from dmff.admp.spatial import v_pbc_shift +from functools import partial + +# for debug +# from jax_md import partition, space +# from admp.parser import * +# from admp.multipole import * +# from jax import grad, value_and_grad +# from admp.pme import * + +# jitted and vmapped parameter distributors +# all three look identical, but they assume different input shapes +# you should use different functions for different inputs, to avoid recompiling +@partial(vmap, in_axes=(None, 0), out_axes=(0)) +@jit_condition(static_argnums=()) +def distribute_scalar(params, index): + return params[index] + + +@partial(vmap, in_axes=(None, 0), out_axes=(0)) +@jit_condition(static_argnums=()) +def distribute_v3(pos, index): + return pos[index] + + +@partial(vmap, in_axes=(None, 0), out_axes=(0)) +@jit_condition(static_argnums=()) +def distribute_multipoles(multipoles, index): + return multipoles[index] + + +@partial(vmap, in_axes=(None, 0), out_axes=(0)) +@jit_condition(static_argnums=()) +def distribute_dispcoeff(c_list, index): + return c_list[index] + + +def generate_pairwise_interaction(pair_int_kernel, covalent_map, static_args): + ''' + This is a calculator generator for pairwise interaction + + Input: + pair_int_kernel: + function type (dr, m, p1i, p1j, p2i, p2j) -> energy : the vectorized kernel function, + dr is the distance, m is the topological scaling factor, p1i, p1j, p2i, p2j are pairwise parameters + + covalent_map: + Na * Na, int: the covalent_map matrix that marks the topological distances between atoms + + static_args: + dict: a dictionary that stores all static global parameters (such as lmax, kappa, etc) + + Output: + pair_int: + function type (positions, box, pairs, mScales, p1, p2, ...) -> energy + The pair interaction calculator. p1, p2 ... involved atomic parameters, the order should be consistent + with the order in kernel + ''' + + def pair_int(positions, box, pairs, mScales, *atomic_params): + pairs = pairs[pairs[:, 0] < pairs[:, 1]] + ri = distribute_v3(positions, pairs[:, 0]) + rj = distribute_v3(positions, pairs[:, 1]) + # ri = positions[pairs[:, 0]] + # rj = positions[pairs[:, 1]] + nbonds = covalent_map[pairs[:, 0], pairs[:, 1]] + mscales = distribute_scalar(mScales, nbonds-1) + # mscales = mScales[nbonds-1] + box_inv = jnp.linalg.inv(box) + dr = ri - rj + dr = v_pbc_shift(dr, box, box_inv) + dr = jnp.linalg.norm(dr, axis=1) + + pair_params = [] + for i, param in enumerate(atomic_params): + pair_params.append(distribute_scalar(param, pairs[:, 0])) + pair_params.append(distribute_scalar(param, pairs[:, 1])) + # pair_params.append(param[pairs[:, 0]]) + # pair_params.append(param[pairs[:, 1]]) + + energy = jnp.sum(pair_int_kernel(dr, mscales, *pair_params)) + return energy + + return pair_int + + +@vmap +@jit_condition(static_argnums={}) +def TT_damping_qq_c6_kernel(dr, m, ai, aj, bi, bj, qi, qj, ci, cj): + a = jnp.sqrt(ai * aj) + b = jnp.sqrt(bi * bj) + c = ci * cj + q = qi * qj + r = dr * 1.889726878 # convert to bohr + br = b * r + br2 = br * br + br3 = br2 * br + br4 = br3 * br + br5 = br4 * br + br6 = br5 * br + exp_br = jnp.exp(-br) + f = 2625.5 * a * exp_br \ + + (-2625.5) * exp_br * (1+br) * q / br \ + + exp_br*(1+br+br2/2+br3/6+br4/24+br5/120+br6/720) * c / dr**6 + + return f * m + + +def validation(pdb): + xml = 'mpidwater.xml' + pdbinfo = read_pdb(pdb) + serials = pdbinfo['serials'] + names = pdbinfo['names'] + resNames = pdbinfo['resNames'] + resSeqs = pdbinfo['resSeqs'] + positions = pdbinfo['positions'] + box = pdbinfo['box'] # a, b, c, α, β, γ + charges = pdbinfo['charges'] + positions = jnp.asarray(positions) + lx, ly, lz, _, _, _ = box + box = jnp.eye(3)*jnp.array([lx, ly, lz]) + + mScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + pScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + dScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + + rc = 4 # in Angstrom + ethresh = 1e-4 + + n_atoms = len(serials) + + atomTemplate, residueTemplate = read_xml(xml) + atomDicts, residueDicts = init_residues(serials, names, resNames, resSeqs, positions, charges, atomTemplate, residueTemplate) + + covalent_map = assemble_covalent(residueDicts, n_atoms) + displacement_fn, shift_fn = space.periodic_general(box, fractional_coordinates=False) + neighbor_list_fn = partition.neighbor_list(displacement_fn, box, rc, 0, format=partition.OrderedSparse) + nbr = neighbor_list_fn.allocate(positions) + pairs = nbr.idx.T + + pmax = 10 + kappa, K1, K2, K3 = setup_ewald_parameters(rc, ethresh, box) + kappa = 0.657065221219616 + + # construct the C list + c_list = np.zeros((3, n_atoms)) + a_list = np.zeros(n_atoms) + q_list = np.zeros(n_atoms) + b_list = np.zeros(n_atoms) + nmol=int(n_atoms/3) + for i in range(nmol): + a = i*3 + b = i*3+1 + c = i*3+2 + # dispersion coeff + c_list[0][a]=37.199677405 + c_list[0][b]=7.6111103 + c_list[0][c]=7.6111103 + c_list[1][a]=85.26810658 + c_list[1][b]=11.90220148 + c_list[1][c]=11.90220148 + c_list[2][a]=134.44874488 + c_list[2][b]=15.05074749 + c_list[2][c]=15.05074749 + # q + q_list[a] = -0.741706 + q_list[b] = 0.370853 + q_list[c] = 0.370853 + # b, Bohr^-1 + b_list[a] = 2.00095977 + b_list[b] = 1.999519942 + b_list[c] = 1.999519942 + # a, Hartree + a_list[a] = 458.3777 + a_list[b] = 0.0317 + a_list[c] = 0.0317 + + + c_list = jnp.array(c_list) + +# @partial(vmap, in_axes=(0, 0, 0, 0), out_axes=(0)) +# @jit_condition(static_argnums=()) +# def disp6_pme_real_kernel(dr, m, ci, cj): +# # unpack static arguments +# kappa = static_args['kappa'] +# # calculate distance +# dr2 = dr ** 2 +# dr6 = dr2 ** 3 +# # do calculation +# x2 = kappa**2 * dr2 +# exp_x2 = jnp.exp(-x2) +# x4 = x2 * x2 +# g = (1 + x2 + 0.5*x4) * exp_x2 +# return (m + g - 1) * ci * cj / dr6 + +# static_args = {'kappa': kappa} +# disp6_pme_real = generate_pairwise_interaction(disp6_pme_real_kernel, covalent_map, static_args) +# print(disp6_pme_real(positions, box, pairs, mScales, c_list[0, :])) + + TT_damping_qq_c6 = generate_pairwise_interaction(TT_damping_qq_c6_kernel, covalent_map, static_args={}) + + TT_damping_qq_c6(positions, box, pairs, mScales, a_list, b_list, q_list, c_list[0]) + print('ok') + print(TT_damping_qq_c6(positions, box, pairs, mScales, a_list, b_list, q_list, c_list[0])) + return + + +# below is the validation code +if __name__ == '__main__': + validation(sys.argv[1]) diff --git a/dmff/admp/parser.py b/dmff/admp/parser.py new file mode 100644 index 000000000..9ba07239b --- /dev/null +++ b/dmff/admp/parser.py @@ -0,0 +1,477 @@ +# jichen: deprecated +from xml.dom import minidom +import numpy as np +import warnings +from collections import defaultdict + +def read_atom_line(line_full): + """ + Read atom line from pdb format + HETATM 1 H14 ORTE 0 6.301 0.693 1.919 1.00 0.00 H + + 1-6 7-11 13-16 17 18-20 22 23-26 27 28-30 31-38 39-46 47-54 55-60 61-66 67-72 73-76 77-78 79-80 + ATOM serial name altLoc resName chainID resSeq iCode _ x y z occupancy tempFactor _ segID element charge + """ + + line = line_full.rstrip("\n") + type_atm = line[0:6] + if type_atm == "ATOM " or type_atm == "HETATM": + + # Roy + serial = line[7:12].strip() + + name = line[12:16].strip() + + altLoc = line[16] + resName = line[17:21] + chainID = line[21] # Not used + + resSeq = int(line[22:26].split()[0]) # sequence identifier + iCode = line[26] # insertion code, not used + + # atomic coordinates + try: + coord = np.array( + [float(line[30:38]), float(line[38:46]), float(line[46:54])], + dtype=np.float64, + ) + except ValueError: + raise ValueError("Invalid or missing coordinate(s)") + + # occupancy & B factor + try: + occupancy = float(line[54:60]) + except ValueError: + occupancy = None # Rather than arbitrary zero or one + + if occupancy is not None and occupancy < 0: + warnings.warn("Negative occupancy in one or more atoms") + + try: + bfactor = float(line[60:66]) + except ValueError: + # The PDB use a default of zero if the data is missing + bfactor = 0.0 + + segid = line[72:76] # not used + element = line[76:78].strip().upper() + charge = line[79:81] + + else: + raise ValueError("Only ATOM and HETATM supported") + + return ( + type_atm, + serial, + name, + altLoc, + resName.strip(), + chainID, + resSeq, + iCode, + coord, + occupancy, + bfactor, + segid, + element, + charge, + ) + +def read_pdb(file): + """Read PDB files.""" + fileobj = open(file, 'r') + orig = np.identity(3) + trans = np.zeros(3) + serials = [] + names = [] + altLocs = [] + resNames = [] + chainIDs = [] + resSeqs = [] + iCodes = [] + positions = [] + occupancies = [] + tempFactors = [] + segId = [] + elements = [] + charges = [] + cell = None + pbc = None + cellpar = [] + conects = {} + # make sure that only one frame is read + continue_read_atoms_flag = True + # serial starts at 1 and we need to discard it and just keep align with positions + id = 0 + + for line in fileobj.readlines(): + if line.startswith('CRYST1'): + cellpar = [float(line[6:15]), # a + float(line[15:24]), # b + float(line[24:33]), # c + float(line[33:40]), # alpha + float(line[40:47]), # beta + float(line[47:54])] # gamma + + for c in range(3): + if line.startswith('ORIGX' + '123'[c]): + orig[c] = [float(line[10:20]), + float(line[20:30]), + float(line[30:40])] + trans[c] = float(line[45:55]) + + if ( + line.startswith("ATOM") + or line.startswith("HETATM") + and continue_read_atoms_flag + ): + # Atom name is arbitrary and does not necessarily + # contain the element symbol. The specification + # requires the element symbol to be in columns 77+78. + # Fall back to Atom name for files that do not follow + # the spec, e.g. packmol. + + # line_info = type_atm, serial, name, altLoc, resName, chainID, resSeq, iCode, coord, occupancy, tempFactor, segid, element, charge + line_info = read_atom_line(line) + + # serials.append(int(line_info[1])) + serials.append(id) + id += 1 + names.append(line_info[2]) + resNames.append(line_info[4]) + resSeqs.append(line_info[6]) + position = np.dot(orig, line_info[8]) + trans + positions.append(position) + if line_info[9] is not None: + occupancies.append(line_info[9]) + tempFactors.append(line_info[10]) + elements.append(line_info[-2]) + charges.append(line_info[-1] or 0) + + if line.startswith("END"): + # End of configuration reached + # According to the latest PDB file format (v3.30), + # this line should start with 'ENDMDL' (not 'END'), + # but in this way PDB trajectories from e.g. CP2K + # are supported (also VMD supports this format). + continue_read_atoms_flag = False + pass + + if line.startswith("CONECT"): + l = line.split() + center_atom_idx = int(l[1]) + bonded_atom_idx = [int(i) for i in l[2:]] + + conects[center_atom_idx] = bonded_atom_idx + fileobj.close() + + return {'serials': serials, + 'names': names, + 'resNames': resNames, + 'resSeqs': resSeqs, + 'positions': np.vstack(positions), + 'charges': charges, + 'connects': conects, + 'box': cellpar} + +def set_axis_type(atoms): + ZThenX = 0 + Bisector = 1 + ZBisect = 2 + ThreeFold = 3 + Zonly = 4 + NoAxisType = 5 + LastAxisTypeIndex = 6 + kStrings = ['kz', 'kx', 'ky'] + + for atom in atoms: + atomType = atom['type'] + kIndices = [atomType] + + for kString in kStrings: + + if kString in atom and atom[kString] != '': + kIndices.append(atom[kString]) + atom['axis_indices'] = kIndices + + # set axis type + + kIndicesLen = len(kIndices) + + if (kIndicesLen > 3): + ky = kIndices[3] + kyNegative = False + if ky.startswith('-'): + ky = kIndices[3] = ky[1:] + kyNegative = True + else: + ky = "" + + if (kIndicesLen > 2): + kx = kIndices[2] + kxNegative = False + if kx.startswith('-'): + kx = kIndices[2] = kx[1:] + kxNegative = True + else: + kx = "" + + if (kIndicesLen > 1): + kz = kIndices[1] + kzNegative = False + if kz.startswith('-'): + kz = kIndices[1] = kz[1:] + kzNegative = True + else: + kz = "" + + while(len(kIndices) < 4): + kIndices.append("") + + axisType = ZThenX + if (not kz): + axisType = NoAxisType + if (kz and not kx): + axisType = Zonly + if (kz and kzNegative or kx and kxNegative): + axisType = Bisector + if (kx and kxNegative and ky and kyNegative): + axisType = ZBisect + if (kz and kzNegative and kx and kxNegative and ky and kyNegative): + axisType = ThreeFold + + atom['axisType'] = axisType + + return atoms + +def read_xml(fileobj): + + fileobj = minidom.parse(fileobj) + + multipoles = fileobj.getElementsByTagName("Multipole") + + residueTemplates = [] + atomTemplates = [] + + for r in fileobj.getElementsByTagName('Residue'): + + resName = r.getAttribute("name") + residueTemplate = {'resName': resName, 'atoms': [], } + + + for a in r.getElementsByTagName('Atom'): + atomName = a.getAttribute('name') + atomType = a.getAttribute('type') + atomTemplate = {'name': atomName, 'type': atomType} + + residueTemplate['atoms'].append(atomTemplate) + atomTemplates.append(atomTemplate) + + topo = defaultdict(list) + for b in r.getElementsByTagName('Bond'): + + from_ = b.getAttribute('from') + to_ = b.getAttribute('to') + topo[from_].append(to_) + # topo[to_].append(from_) + + residueTemplate['topo'] = dict(topo) + residueTemplates.append(residueTemplate) + + for i, multipole in enumerate(multipoles): + + multiDict = { + "c0": float(multipole.getAttribute("c0")), + "dX": float(multipole.getAttribute("dX")), + "dY": float(multipole.getAttribute("dY")), + "dZ": float(multipole.getAttribute("dZ")), + "qXX": float(multipole.getAttribute("qXX")), + "qXY": float(multipole.getAttribute("qXY")), + "qYY": float(multipole.getAttribute("qYY")), + "qXZ": float(multipole.getAttribute("qXZ")), + "qYZ": float(multipole.getAttribute("qYZ")), + "qZZ": float(multipole.getAttribute("qZZ")), + "oXXX": float(multipole.getAttribute("oXXX")), + "oXXY": float(multipole.getAttribute("oXXY")), + "oXYY": float(multipole.getAttribute("oXYY")), + "oYYY": float(multipole.getAttribute("oYYY")), + "oXXZ": float(multipole.getAttribute("oXXZ")), + "oXYZ": float(multipole.getAttribute("oXYZ")), + "oYYZ": float(multipole.getAttribute("oYYZ")), + "oXZZ": float(multipole.getAttribute("oXZZ")), + "oYZZ": float(multipole.getAttribute("oYZZ")), + "oZZZ": float(multipole.getAttribute("oZZZ")), + "kx": multipole.getAttribute("kx"), + "kz": multipole.getAttribute("kz"), + "ky": multipole.getAttribute("ky") + } + + for template in atomTemplates: + if template['type'] == multipole.getAttribute("type"): + template.update(multiDict) + + + for p in fileobj.getElementsByTagName('Polarize'): + + pxx = p.getAttribute('polarizabilityXX') + pyy = p.getAttribute('polarizabilityYY') + pzz = p.getAttribute('polarizabilityZZ') + thole = p.getAttribute('thole') + polarDict = {'polarizabilityXX': pxx, 'polarizabilityYY': pyy, 'polarizabilityZZ':pzz, 'thole': thole} + + for template in atomTemplates: + if template['type'] == p.getAttribute('type'): + template.update(polarDict) + + set_axis_type(atomTemplates) + + return atomTemplates, residueTemplates + +class Atom: + + def __init__(self, serial, name, resName, resSeq, position, charge, ) -> None: + self.serial = serial + self.name = name + self.position = position + self.charge = charge + self.resName = resName + self.charge = charge + self.linkAtom = [] + self.resSeq = resSeq + + def __eq__(self, o): + return o.serial == self.serial + + def link(self, atom): + if atom not in self.linkAtom: + self.linkAtom.append(atom) + if self not in atom.linkAtom: + atom.linkAtom.append(self) + + def __repr__(self) -> str: + return f'< Atom{self.serial}: {self.name} >' + +class Residue: + + def __init__(self, resName, resSeq) -> None: + self.resName = resName + self.resSeq = resSeq + self.atoms = {} + self.topo = [] + self.covalent_map = {} + + def add(self, serial, atom): + self.atoms[serial] = atom + + def __next__(self): + return next(self.atoms) + + def __getitem__(self, name): + for atom in self.atoms.values(): + if atom.name == name: + return atom + + def __repr__(self) -> str: + return f'< Residue{self.resSeq}: {self.resName} >' + +def init_residues(serials, names, resNames, resSeqs, positions, charges, atomTemplates, residueTemplates): + + residueDicts = {} + atomDicts = {} + + for name, seq in zip(resNames, resSeqs): + if seq not in residueDicts: + residueDicts[seq] = Residue(name, seq) + + + # build up residue + for serial, name, resName, resSeq, position, charge in zip(serials, names, resNames, resSeqs, positions, charges): + + atom = Atom(serial, name, resName, resSeq, position, charge) + + for a in atomTemplates: + if name == a['name']: + for k, v in a.items(): + setattr(atom, k, v) + atomDicts[serial] = atom + + residueDicts[resSeq].add(atom.serial, atom) + + + # build up topo + for residue in residueDicts.values(): + + for residueTemplate in residueTemplates: + if residueTemplate['resName'] == residue.resName: + template = residueTemplate + + for c, p in template['topo'].items(): + ctemp = template['atoms'][int(c)] + catom = residue[ctemp['name']] + + for pp in p: + ptemp = template['atoms'][int(pp)] + patom = residue[ptemp['name']] + catom.link(patom) + + # build up axis indices + for residue in residueDicts.values(): + + for atom in residue.atoms.values(): + indices = [index if index != '' else -1 for index in atom.axis_indices[1: ]] + + for patom in residue.atoms.values(): + if patom.serial == atom.serial: + continue + for i in range(len(indices)): + if indices[i] == patom.type: + indices[i] = patom.serial + break + + atom.axis_indices = indices + + + # build up covalent map in residue + for i in residue.atoms.values(): + visited = [i.serial] + residue.covalent_map[i.serial] = {} + for j in i.linkAtom: + residue.covalent_map[i.serial][j.serial] = 1 + visited.append(j.serial) + for k in j.linkAtom: + if k.serial not in visited: + residue.covalent_map[i.serial][k.serial] = 2 + visited.append(k.serial) + else: + continue + for l in k.linkAtom: + if l.serial not in visited: + residue.covalent_map[i.serial][l.serial] = 3 + visited.append(l.serial) + else: + continue + for m in l.linkAtom: + if m.serial not in visited: + residue.covalent_map[i.serial][m.serial] = 4 + visited.append(m.serial) + else: + continue + + return atomDicts, residueDicts + +def assemble_covalent(residueDicts, natoms): + + covalents = [c.covalent_map for c in residueDicts.values()] + + covalent_map = np.zeros((natoms, natoms), dtype=int) + + for covalent in covalents: + + for c, p in covalent.items(): + + for pp, dr in p.items(): + + covalent_map[c][pp] = dr + + return covalent_map + \ No newline at end of file diff --git a/dmff/admp/pme.py b/dmff/admp/pme.py new file mode 100755 index 000000000..f973d85a1 --- /dev/null +++ b/dmff/admp/pme.py @@ -0,0 +1,850 @@ +import sys +import numpy as np +import jax +import jax.numpy as jnp +from jax import grad, value_and_grad, vmap, jit +from jax.scipy.special import erf +from dmff.settings import DO_JIT +from dmff.admp.settings import POL_CONV, MAX_N_POL +from dmff.utils import jit_condition +from dmff.admp.multipole import C1_c2h, convert_cart2harm +from dmff.admp.multipole import rot_ind_global2local, rot_global2local, rot_local2global +from dmff.admp.spatial import v_pbc_shift, generate_construct_local_frames, build_quasi_internal +from dmff.admp.pairwise import distribute_scalar, distribute_v3, distribute_multipoles +from functools import partial + +DIELECTRIC = 1389.35455846 +DEFAULT_THOLE_WIDTH = 0.3 + +from dmff.admp.recip import generate_pme_recip, Ck_1 + +# for debugging use only +# from jax_md import partition, space +# from admp.parser import * + +# from jax.config import config +# config.update("jax_enable_x64", True) + +# Functions that are related to electrostatic pme + +class ADMPPmeForce: + ''' + This is a convenient wrapper for multipolar PME calculations + It wrapps all the environment parameters of multipolar PME calculation + The so called "environment paramters" means parameters that do not need to be differentiable + ''' + + def __init__(self, box, axis_type, axis_indices, covalent_map, rc, ethresh, lmax, lpol=False): + self.axis_type = axis_type + self.axis_indices = axis_indices + self.rc = rc + self.ethresh = ethresh + self.lmax = int(lmax) # jichen: type checking + kappa, K1, K2, K3 = setup_ewald_parameters(rc, ethresh, box) + self.kappa = kappa + self.K1 = K1 + self.K2 = K2 + self.K3 = K3 + self.pme_order = 6 + self.covalent_map = covalent_map + self.lpol = lpol + self.n_atoms = int(covalent_map.shape[0]) # len(axis_type) + + # setup calculators + self.refresh_calculators() + return + + + def generate_get_energy(self): + # if the force field is not polarizable + if not self.lpol: + def get_energy(positions, box, pairs, Q_local, mScales): + return energy_pme(positions, box, pairs, + Q_local, None, None, None, + mScales, None, None, self.covalent_map, + self.construct_local_frames, self.pme_recip, + self.kappa, self.K1, self.K2, self.K3, self.lmax, False) + return get_energy + else: + # this is the bare energy calculator, with Uind as explicit input + def energy_fn(positions, box, pairs, Q_local, Uind_global, pol, tholes, mScales, pScales, dScales): + return energy_pme(positions, box, pairs, + Q_local, Uind_global, pol, tholes, + mScales, pScales, dScales, self.covalent_map, + self.construct_local_frames, self.pme_recip, + self.kappa, self.K1, self.K2, self.K3, self.lmax, True) + self.energy_fn = energy_fn + self.grad_U_fn = grad(self.energy_fn, argnums=(4)) + self.grad_pos_fn = grad(self.energy_fn, argnums=(0)) + self.U_ind = jnp.zeros((self.n_atoms, 3)) + # this is the wrapper that include a Uind optimizer + def get_energy(positions, box, pairs, Q_local, pol, tholes, mScales, pScales, dScales, U_init=self.U_ind): + self.U_ind, self.lconverg, self.n_cycle = self.optimize_Uind(positions, box, pairs, Q_local, pol, tholes, mScales, pScales, dScales, U_init=U_init) + # here we rely on Feynman-Hellman theorem, drop the term dV/dU*dU/dr ! + # self.U_ind = jax.lax.stop_gradient(U_ind) + return self.energy_fn(positions, box, pairs, Q_local, self.U_ind, pol, tholes, mScales, pScales, dScales) + return get_energy + + + def update_env(self, attr, val): + ''' + Update the environment of the calculator + ''' + setattr(self, attr, val) + self.refresh_calculators() + + + def refresh_calculators(self): + ''' + refresh the energy and force calculators according to the current environment + ''' + if self.lmax > 0: + self.construct_local_frames = generate_construct_local_frames(self.axis_type, self.axis_indices) + else: + self.construct_local_frames = None + self.pme_recip = generate_pme_recip(Ck_1, self.kappa, False, self.pme_order, self.K1, self.K2, self.K3, self.lmax) + # generate the force calculator + self.get_energy = self.generate_get_energy() + self.get_forces = value_and_grad(self.get_energy) + return + + def optimize_Uind(self, positions, box, pairs, Q_local, pol, tholes, mScales, pScales, dScales, U_init=None, maxiter=MAX_N_POL, thresh=POL_CONV): + ''' + This function converges the induced dipole + Note that we cut all the gradient chain passing through this function as we assume Feynman-Hellman theorem + Gradients related to Uind should be dropped + ''' + # Do not track gradient in Uind optimization + positions = jax.lax.stop_gradient(positions) + box = jax.lax.stop_gradient(box) + Q_local = jax.lax.stop_gradient(Q_local) + pol = jax.lax.stop_gradient(pol) + tholes = jax.lax.stop_gradient(tholes) + mScales = jax.lax.stop_gradient(mScales) + pScales = jax.lax.stop_gradient(pScales) + dScales = jax.lax.stop_gradient(dScales) + if U_init is None: + U = jnp.zeros((self.n_atoms, 3)) + else: + U = U_init + site_filter = (pol>0.001) # focus on the actual polarizable sites + + for i in range(maxiter): + field = self.grad_U_fn(positions, box, pairs, Q_local, U, pol, tholes, mScales, pScales, dScales) + E = self.energy_fn(positions, box, pairs, Q_local, U, pol, tholes, mScales, pScales, dScales) + # print(i, E, jnp.max(jnp.abs(field[site_filter]))) + if jnp.max(jnp.abs(field[site_filter])) < thresh: + break + U = U - field * pol[:, jnp.newaxis] / DIELECTRIC + if i == maxiter-1: + flag = False + else: # converged + flag = True + return U, flag, i + + +def setup_ewald_parameters(rc, ethresh, box): + ''' + Given the cutoff distance, and the required precision, determine the parameters used in + Ewald sum, including: kappa, K1, K2, and K3. + The algorithm is exactly the same as OpenMM, see: + http://docs.openmm.org/latest/userguide/theory.html + + Input: + rc: + float, the cutoff distance + ethresh: + float, required energy precision + box: + 3*3 matrix, box size, a, b, c arranged in rows + + Output: + kappa: + float, the attenuation factor + K1, K2, K3: + integers, sizes of the k-points mesh + ''' + kappa = jnp.sqrt(-jnp.log(2*ethresh))/rc + K1 = jnp.ceil(2 * kappa * box[0, 0] / 3 / ethresh**0.2) + K2 = jnp.ceil(2 * kappa * box[1, 1] / 3 / ethresh**0.2) + K3 = jnp.ceil(2 * kappa * box[2, 2] / 3 / ethresh**0.2) + + return kappa, int(K1), int(K2), int(K3) + + +# @jit_condition(static_argnums=()) +def energy_pme(positions, box, pairs, + Q_local, Uind_global, pol, tholes, + mScales, pScales, dScales, covalent_map, + construct_local_frame_fn, pme_recip_fn, kappa, K1, K2, K3, lmax, lpol): + ''' + This is the top-level wrapper for multipole PME + + Input: + positions: + Na * 3: positions + box: + 3 * 3: box + Q_local: + Na * (lmax+1)^2: harmonic multipoles of each site in local frame + Uind_global: + Na * 3: the induced dipole moment, in GLOBAL CARTESIAN! + pol: + (Na,) float: the polarizability of each site, unit in A**3 + tholes: + (Na,) float: the thole damping widths for each atom, it's dimensionless, default is 8 according to MPID paper + mScales, pScale, dScale: + (Nexcl,): multipole-multipole interaction exclusion scalings: 1-2, 1-3 ... + for permanent-permanent, permanent-induced, induced-induced interactions + pairs: + Np * 2: interacting pair indices + covalent_map: + Na * Na: topological distances between atoms, if i, j are topologically distant, then covalent_map[i, j] == 0 + construct_local_frame_fn: + function: local frame constructors, from generate_local_frame_constructor + pme_recip: + function: see recip.py, a reciprocal space calculator + kappa: + float: kappa in A^-1 + K1, K2, K3: + int: max K for reciprocal calculations + lmax: + int: maximum L + bool: + int: if polarizable or not? if yes, 1, otherwise 0 + + Output: + energy: total pme energy + ''' + # if doing a multipolar calculation + if lmax > 0: + local_frames = construct_local_frame_fn(positions, box) + Q_global = rot_local2global(Q_local, local_frames, lmax) + else: + if lpol: + # if fixed multipole only contains charge, and it's polarizable, then expand Q matrix + dips = jnp.zeros((Q_local.shape[0], 3)) + Q_global = jnp.hstack((Q_global, dips)) + lmax = 1 + else: + Q_global = Q_local + + # note we assume when lpol is True, lmax should be >= 1 + if lpol: + # convert Uind to global harmonics, in accord with Q_global + U_ind = C1_c2h.dot(Uind_global.T).T + Q_global_tot = Q_global.at[:, 1:4].add(U_ind) + else: + Q_global_tot = Q_global + + if lpol: + ene_real = pme_real(positions, box, pairs, Q_global, U_ind, pol, tholes, + mScales, pScales, dScales, covalent_map, kappa, lmax, True) + else: + ene_real = pme_real(positions, box, pairs, Q_global, None, None, None, + mScales, None, None, covalent_map, kappa, lmax, False) + + ene_recip = pme_recip_fn(positions, box, Q_global_tot) + + ene_self = pme_self(Q_global_tot, kappa, lmax) + + if lpol: + ene_self += pol_penalty(U_ind, pol) + + return ene_real + ene_recip + ene_self + + +# @partial(vmap, in_axes=(0, 0, None, None), out_axes=0) +@jit_condition(static_argnums=(3)) +def calc_e_perm(dr, mscales, kappa, lmax=2): + + ''' + This function calculates the ePermCoefs at once + ePermCoefs is basically the interaction tensor between permanent multipole components + Everything should be done in the so called quasi-internal (qi) frame + Energy = \sum_ij qiQI * ePermCoeff_ij * qiQJ + + Inputs: + dr: + float: distance between one pair of particles + mscales: + float: scaling factor between permanent - permanent multipole interactions, for each pair + kappa: + float: \kappa in PME, unit in A^-1 + lmax: + int: max L + + Output: + cc, cd, dd_m0, dd_m1, cq, dq_m0, dq_m1, qq_m0, qq_m1, qq_m2: + n * 1 array: ePermCoefs + ''' + + # be aware of unit and dimension !! + rInv = 1 / dr + rInvVec = jnp.array([DIELECTRIC*(rInv**i) for i in range(0, 9)]) + alphaRVec = jnp.array([(kappa*dr)**i for i in range(0, 10)]) + X = 2 * jnp.exp(-alphaRVec[2]) / jnp.sqrt(np.pi) + tmp = jnp.array(alphaRVec[1]) + doubleFactorial = 1 + facCount = 1 + erfAlphaR = erf(alphaRVec[1]) + + # bVec = jnp.empty((6, len(erfAlphaR))) + bVec = jnp.empty(6) + + bVec = bVec.at[1].set(-erfAlphaR) + for i in range(2, 6): + bVec = bVec.at[i].set((bVec[i-1]+(tmp*X/doubleFactorial))) + facCount += 2 + doubleFactorial *= facCount + tmp *= 2 * alphaRVec[2] + + # C-C: 1 + cc = rInvVec[1] * (mscales + bVec[2] - alphaRVec[1]*X) + if lmax >= 1: + # C-D + cd = rInvVec[2] * (mscales + bVec[2]) + # D-D: 2 + dd_m0 = -2/3 * rInvVec[3] * (3*(mscales + bVec[3]) + alphaRVec[3]*X) + dd_m1 = rInvVec[3] * (mscales + bVec[3] - (2/3)*alphaRVec[3]*X) + else: + cd = 0 + dd_m0 = 0 + dd_m1 = 0 + + if lmax >= 2: + ## C-Q: 1 + cq = (mscales + bVec[3]) * rInvVec[3] + ## D-Q: 2 + dq_m0 = rInvVec[4] * (3* (mscales + bVec[3]) + (4/3) * alphaRVec[5]*X) + dq_m1 = -jnp.sqrt(3) * rInvVec[4] * (mscales + bVec[3]) + ## Q-Q + qq_m0 = rInvVec[5] * (6* (mscales + bVec[4]) + (4/45)* (-3 + 10*alphaRVec[2]) * alphaRVec[5]*X) + qq_m1 = - (4/15) * rInvVec[5] * (15*(mscales+bVec[4]) + alphaRVec[5]*X) + qq_m2 = rInvVec[5] * (mscales + bVec[4] - (4/15)*alphaRVec[5]*X) + else: + cq = 0 + dq_m0 = 0 + dq_m1 = 0 + qq_m0 = 0 + qq_m1 = 0 + qq_m1 = 0 + qq_m2 = 0 + + return cc, cd, dd_m0, dd_m1, cq, dq_m0, dq_m1, qq_m0, qq_m1, qq_m2 + + +@jit_condition(static_argnums=()) +def switch_val(x, x0, sigma, y0, y1): + ''' + This is a Fermi function switches between y0 and y1, according to the value of x + y = y0 when x << x0 + y = y1 when x >> x1 + sigma control sthe switch width + ''' + u = (x-x0) / sigma + w0 = 1 / (jnp.exp(u) + 1) + w1 = 1 - w0 + return w0*y0 + w1*y1 + + +def gen_trim_val_0(thresh): + ''' + Trim the value at zero point to avoid singularity + ''' + def trim_val_0(x): + return jnp.piecewise(x, [x=thresh], [lambda x: jnp.array(thresh), lambda x: x]) + if DO_JIT: + return jit(trim_val_0) + else: + return trim_val_0 + +trim_val_0 = gen_trim_val_0(1e-8) + + +def gen_trim_val_infty(thresh): + ''' + Trime the value at infinity to avoid divergence + ''' + def trim_val_infty(x): + return jnp.piecewise(x, [x=thresh], [lambda x: x, lambda x: jnp.array(thresh)]) + if DO_JIT: + return jit(trim_val_infty) + else: + return trim_val_infty + +trim_val_infty = gen_trim_val_infty(1e8) + + +@jit_condition(static_argnums=(7)) +def calc_e_ind(dr, thole1, thole2, dmp, pscales, dscales, kappa, lmax=2): + + ''' + This function calculates the eUindCoefs at once + ## compute the Thole damping factors for energies + eUindCoefs is basically the interaction tensor between permanent multipole components and induced dipoles + Everything should be done in the so called quasi-internal (qi) frame + + + Inputs: + dr: + float: distance between one pair of particles + dmp + float: damping factors between one pair of particles + mscales: + float: scaling factor between permanent - permanent multipole interactions, for each pair + pscales: + float: scaling factor between permanent - induced multipole interactions, for each pair + au: + float: for damping factors + kappa: + float: \kappa in PME, unit in A^-1 + lmax: + int: max L + + Output: + Interaction tensors components + ''' + ## switch function + + # a = 1/(jnp.exp((pscales-0.001)*10000)+1) * (thole1 + thole2) + 8/(jnp.exp((-pscales+0.01)*10000)+1) + a = switch_val(pscales, 1e-3, 1e-5, DEFAULT_THOLE_WIDTH, thole1+thole2) + + dmp = trim_val_0(dmp) + u = trim_val_infty(dr/dmp) + + ## au <= 50 aupi = au ;au> 50 aupi = 50 + au = a * u + expau = jnp.piecewise(au, [au<50, au>=50], [lambda au: jnp.exp(-au), lambda au: jnp.array(0)]) + + ## compute the Thole damping factors for energies + au2 = trim_val_infty(au*au) + au3 = trim_val_infty(au2*au) + au4 = trim_val_infty(au3*au) + au5 = trim_val_infty(au4*au) + au6 = trim_val_infty(au5*au) + + ## Thole damping factors for energies + thole_c = 1.0 - expau*(1.0 + au + 0.5*au2) + thole_d0 = 1.0 - expau*(1.0 + au + 0.5*au2 + au3/4.0) + thole_d1 = 1.0 - expau*(1.0 + au + 0.5*au2) + thole_q0 = 1.0 - expau*(1.0 + au + 0.5*au2 + au3/6.0 + au4/18.0) + thole_q1 = 1.0 - expau*(1.0 + au + 0.5*au2 + au3/6.0) + # copied from calc_e_perm + # be aware of unit and dimension !! + rInv = 1 / dr + rInvVec = jnp.array([DIELECTRIC*(rInv**i) for i in range(0, 9)]) + alphaRVec = jnp.array([(kappa*dr)**i for i in range(0, 10)]) + X = 2 * jnp.exp(-alphaRVec[2]) / jnp.sqrt(np.pi) + tmp = jnp.array(alphaRVec[1]) + doubleFactorial = 1 + facCount = 1 + erfAlphaR = erf(alphaRVec[1]) + + #bVec = jnp.empty((6, len(erfAlphaR))) + bVec = jnp.empty(6) + + bVec = bVec.at[1].set(-erfAlphaR) + for i in range(2, 6): + bVec = bVec.at[i].set((bVec[i-1]+(tmp*X/doubleFactorial))) + facCount += 2 + doubleFactorial *= facCount + tmp *= 2 * alphaRVec[2] + + ## C-Uind + cud = 2.0*rInvVec[2]*(pscales*thole_c + bVec[2]) + if lmax >= 1: + ## D-Uind terms + dud_m0 = -2.0*2.0/3.0*rInvVec[3]*(3.0*(pscales*thole_d0 + bVec[3]) + alphaRVec[3]*X) + dud_m1 = 2.0*rInvVec[3]*(pscales*thole_d1 + bVec[3] - 2.0/3.0*alphaRVec[3]*X) + else: + dud_m0 = 0.0 + dud_m1 = 0.0 + + if lmax >= 2: + ## Uind-Q + udq_m0 = 2.0*rInvVec[4]*(3.0*(pscales*thole_q0 + bVec[3]) + 4/3*alphaRVec[5]*X) + udq_m1 = -2.0*jnp.sqrt(3)*rInvVec[4]*(pscales*thole_q1 + bVec[3]) + else: + udq_m0 = 0.0 + udq_m1 = 0.0 + ## Uind-Uind + uscales = 1 + udud_m0 = -2.0/3.0*rInvVec[3]*(3.0*(uscales*thole_d0 + bVec[3]) + alphaRVec[3]*X) + udud_m1 = rInvVec[3]*(uscales*thole_d1 + bVec[3] - 2.0/3.0*alphaRVec[3]*X) + return cud, dud_m0, dud_m1, udq_m0, udq_m1, udud_m0, udud_m1 + + + +@partial(vmap, in_axes=(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, None, None, None), out_axes=0) +@jit_condition(static_argnums=(12, 13)) +def pme_real_kernel(dr, qiQI, qiQJ, qiUindI, qiUindJ, thole1, thole2, dmp, mscales, pscales, dscales, kappa, lmax=2, lpol=False): + ''' + This is the heavy-lifting kernel function to compute the realspace multipolar PME + Vectorized over interacting pairs + + Input: + dr: + float, the interatomic distances, (np) array if vectorized + qiQI: + [(lmax+1)^2] float array, the harmonic multipoles of site i in quasi-internal frame + qiQJ: + [(lmax+1)^2] float array, the harmonic multipoles of site j in quasi-internal frame + qiUindI + (3,) float array, the harmonic dipoles of site i in QI frame + qiUindJ + (3,) float array, the harmonic dipoles of site j in QI frame + thole1 + float: thole damping coeff of site i + thole2 + float: thole damping coeff of site j + dmp: + float: (pol1 * pol2)**1/6, distance rescaling params used in thole damping + mscale: + float, scaling factor between interacting sites (permanent-permanent) + pscale: + float, scaling factor between perm-ind interaction + dscale: + float, scaling factor between ind-ind interaction + kappa: + float, kappa in unit A^1 + lmax: + int, maximum angular momentum + lpol: + bool, doing polarization? + + Output: + energy: + float, realspace interaction energy between the sites + ''' + + cc, cd, dd_m0, dd_m1, cq, dq_m0, dq_m1, qq_m0, qq_m1, qq_m2 = calc_e_perm(dr, mscales, kappa, lmax) + if lpol: + cud, dud_m0, dud_m1, udq_m0, udq_m1, udud_m0, udud_m1 = calc_e_ind(dr, thole1, thole2, dmp, pscales, dscales, kappa, lmax) + + Vij0 = cc*qiQI[0] + Vji0 = cc*qiQJ[0] + # C-Uind + if lpol: + Vij0 -= cud * qiUindI[0] + Vji0 += cud * qiUindJ[0] + + if lmax >= 1: + # C-D + Vij0 = Vij0 - cd*qiQI[1] + Vji1 = -cd*qiQJ[0] + Vij1 = cd*qiQI[0] + Vji0 = Vji0 + cd*qiQJ[1] + # D-D m0 + Vij1 += dd_m0 * qiQI[1] + Vji1 += dd_m0 * qiQJ[1] + # D-D m1 + Vij2 = dd_m1*qiQI[2] + Vji2 = dd_m1*qiQJ[2] + Vij3 = dd_m1*qiQI[3] + Vji3 = dd_m1*qiQJ[3] + # D-Uind + if lpol: + Vij1 += dud_m0 * qiUindI[0] + Vji1 += dud_m0 * qiUindJ[0] + Vij2 += dud_m1 * qiUindI[1] + Vji2 += dud_m1 * qiUindJ[1] + Vij3 += dud_m1 * qiUindI[2] + Vji3 += dud_m1 * qiUindJ[2] + + if lmax >= 2: + # C-Q + Vij0 = Vij0 + cq*qiQI[4] + Vji4 = cq*qiQJ[0] + Vij4 = cq*qiQI[0] + Vji0 = Vji0 + cq*qiQJ[4] + # D-Q m0 + Vij1 += dq_m0*qiQI[4] + Vji4 += dq_m0*qiQJ[1] + # Q-D m0 + Vij4 -= dq_m0*qiQI[1] + Vji1 -= dq_m0*qiQJ[4] + # D-Q m1 + Vij2 = Vij2 + dq_m1*qiQI[5] + Vji5 = dq_m1*qiQJ[2] + Vij3 += dq_m1*qiQI[6] + Vji6 = dq_m1*qiQJ[3] + Vij5 = -(dq_m1*qiQI[2]) + Vji2 += -(dq_m1*qiQJ[5]) + Vij6 = -(dq_m1*qiQI[3]) + Vji3 += -(dq_m1*qiQJ[6]) + # Q-Q m0 + Vij4 += qq_m0*qiQI[4] + Vji4 += qq_m0*qiQJ[4] + # Q-Q m1 + Vij5 += qq_m1*qiQI[5] + Vji5 += qq_m1*qiQJ[5] + Vij6 += qq_m1*qiQI[6] + Vji6 += qq_m1*qiQJ[6] + # Q-Q m2 + Vij7 = qq_m2*qiQI[7] + Vji7 = qq_m2*qiQJ[7] + Vij8 = qq_m2*qiQI[8] + Vji8 = qq_m2*qiQJ[8] + # Q-Uind + if lpol: + Vji4 += udq_m0*qiUindJ[0] + Vij4 -= udq_m0*qiUindI[0] + Vji5 += udq_m1*qiUindJ[1] + Vji6 += udq_m1*qiUindJ[2] + Vij5 -= udq_m1*qiUindI[1] + Vij6 -= udq_m1*qiUindI[2] + + # Uind - Uind + if lpol: + Vij1dd = udud_m0 * qiUindI[0] + Vji1dd = udud_m0 * qiUindJ[0] + Vij2dd = udud_m1 * qiUindI[1] + Vji2dd = udud_m1 * qiUindJ[1] + Vij3dd = udud_m1 * qiUindI[2] + Vji3dd = udud_m1 * qiUindJ[2] + Vijdd = jnp.stack(( Vij1dd, Vij2dd, Vij3dd)) + Vjidd = jnp.stack(( Vji1dd, Vji2dd, Vji3dd)) + + if lmax == 0: + Vij = Vij0 + Vji = Vji0 + elif lmax == 1: + Vij = jnp.stack((Vij0, Vij1, Vij2, Vij3)) + Vji = jnp.stack((Vji0, Vji1, Vji2, Vji3)) + elif lmax == 2: + Vij = jnp.stack((Vij0, Vij1, Vij2, Vij3, Vij4, Vij5, Vij6, Vij7, Vij8)) + Vji = jnp.stack((Vji0, Vji1, Vji2, Vji3, Vji4, Vji5, Vji6, Vji7, Vji8)) + else: + print('Error: Lmax must <= 2') + + if lpol: + return jnp.array(0.5) * (jnp.sum(qiQJ*Vij) + jnp.sum(qiQI*Vji)) + jnp.array(0.5) * (jnp.sum(qiUindJ*Vijdd) + jnp.sum(qiUindI*Vjidd)) + else: + return jnp.array(0.5) * (jnp.sum(qiQJ*Vij) + jnp.sum(qiQI*Vji)) + + +# @jit_condition(static_argnums=(7)) +def pme_real(positions, box, pairs, + Q_global, Uind_global, pol, tholes, + mScales, pScales, dScales, covalent_map, + kappa, lmax, lpol): + ''' + This is the real space PME calculate function + NOTE: only deals with permanent-permanent multipole interactions + It expands the pairwise parameters, and then invoke pme_real_kernel + It seems pointless to jit it: + 1. the heavy-lifting kernel function is jitted and vmapped + 2. len(pairs) keeps changing throughout the simulation, the function would just recompile everytime + + Input: + positions: + Na * 3: positions + box: + 3 * 3: box, axes arranged in row + pairs: + Np * 2: interacting pair indices + Q_global: + Na * (l+1)**2: harmonics multipoles of each atom, in global frame + Uind_global: + Na * 3: harmonic induced dipoles, in global frame + pol: + (Na,): polarizabilities + tholes: + (Na,): thole damping parameters + mScales: + (Nexcl,): permanent multipole-multipole interaction exclusion scalings: 1-2, 1-3 ... + covalent_map: + Na * Na: topological distances between atoms, if i, j are topologically distant, then covalent_map[i, j] == 0 + kappa: + float: kappa in A^-1 + lmax: + int: maximum L + lpol: + Bool: whether do a polarizable calculation? + + Output: + ene: pme realspace energy + ''' + + # expand pairwise parameters, from atomic parameters + pairs = pairs[pairs[:, 0] < pairs[:, 1]] + box_inv = jnp.linalg.inv(box) + r1 = distribute_v3(positions, pairs[:, 0]) + r2 = distribute_v3(positions, pairs[:, 1]) + # r1 = positions[pairs[:, 0]] + # r2 = positions[pairs[:, 1]] + Q_extendi = distribute_multipoles(Q_global, pairs[:, 0]) + Q_extendj = distribute_multipoles(Q_global, pairs[:, 1]) + # Q_extendi = Q_global[pairs[:, 0]] + # Q_extendj = Q_global[pairs[:, 1]] + nbonds = covalent_map[pairs[:, 0], pairs[:, 1]] + indices = nbonds-1 + mscales = distribute_scalar(mScales, indices) + # mscales = mScales[nbonds-1] + if lpol: + pol1 = distribute_scalar(pol, pairs[:, 0]) + pol2 = distribute_scalar(pol, pairs[:, 1]) + thole1 = distribute_scalar(tholes, pairs[:, 0]) + thole2 = distribute_scalar(tholes, pairs[:, 1]) + Uind_extendi = distribute_v3(Uind_global, pairs[:, 0]) + Uind_extendj = distribute_v3(Uind_global, pairs[:, 1]) + pscales = distribute_scalar(pScales, indices) + dscales = distribute_scalar(dScales, indices) + # pol1 = pol[pairs[:,0]] + # pol2 = pol[pairs[:,1]] + # thole1 = tholes[pairs[:,0]] + # thole2 = tholes[pairs[:,1]] + # Uind_extendi = Uind_global[pairs[:, 0]] + # Uind_extendj = Uind_global[pairs[:, 1]] + # pscales = pScales[nbonds-1] + # dscales = dScales[nbonds-1] + dmp = get_pair_dmp(pol1, pol2) + else: + Uind_extendi = None + Uind_extendj = None + pscales = None + dscales = None + thole1 = None + thole2 = None + dmp = None + + # deals with geometries + dr = r1 - r2 + dr = v_pbc_shift(dr, box, box_inv) + norm_dr = jnp.linalg.norm(dr, axis=-1) + Ri = build_quasi_internal(r1, r2, dr, norm_dr) + qiQI = rot_global2local(Q_extendi, Ri, lmax) + qiQJ = rot_global2local(Q_extendj, Ri, lmax) + if lpol: + qiUindI = rot_ind_global2local(Uind_extendi, Ri) + qiUindJ = rot_ind_global2local(Uind_extendj, Ri) + else: + qiUindI = None + qiUindJ = None + + # everything should be pair-specific now + ene = jnp.sum(pme_real_kernel(norm_dr, qiQI, qiQJ, qiUindI, qiUindJ, thole1, thole2, dmp, mscales, pscales, dscales, kappa, lmax, lpol)) + + return ene + + +@partial(vmap, in_axes=(0, 0), out_axes=(0)) +@jit_condition(static_argnums=()) +def get_pair_dmp(pol1, pol2): + return (pol1*pol2) ** (1/6) + + +@jit_condition(static_argnums=(2)) +def pme_self(Q_h, kappa, lmax=2): + ''' + This function calculates the PME self energy + + Inputs: + Q: + Na * (lmax+1)^2: harmonic multipoles, local or global does not matter + kappa: + float: kappa used in PME + + Output: + ene_self: + float: the self energy + ''' + n_harms = (lmax + 1) ** 2 + l_list = np.array([0] + [1,]*3 + [2,]*5)[:n_harms] + l_fac2 = np.array([1] + [3,]*3 + [15,]*5)[:n_harms] + factor = kappa/np.sqrt(np.pi) * (2*kappa**2)**l_list / l_fac2 + return - jnp.sum(factor[np.newaxis] * Q_h**2) * DIELECTRIC + + +@jit_condition(static_argnums=()) +def pol_penalty(U_ind, pol): + ''' + The energy penalty for polarization of each site, currently only supports isotropic polarization: + + Inputs: + U_ind: + Na * 3 float: induced dipoles, in isotropic polarization case, cartesian or harmonic does not matter + pol: + (Na,) float: polarizability + ''' + # this is to remove the singularity when pol=0 + pol_pi = trim_val_0(pol) + # pol_pi = pol/(jnp.exp((-pol+1e-08)*1e10)+1) + 1e-08/(jnp.exp((pol-1e-08)*1e10)+1) + return jnp.sum(0.5/pol_pi*(U_ind**2).T) * DIELECTRIC + + +def validation(pdb): + xml = 'mpidwater.xml' + pdbinfo = read_pdb(pdb) + serials = pdbinfo['serials'] + names = pdbinfo['names'] + resNames = pdbinfo['resNames'] + resSeqs = pdbinfo['resSeqs'] + positions = pdbinfo['positions'] + box = pdbinfo['box'] # a, b, c, α, β, γ + charges = pdbinfo['charges'] + positions = jnp.asarray(positions) + lx, ly, lz, _, _, _ = box + box = jnp.eye(3)*jnp.array([lx, ly, lz]) + + mScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + pScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + dScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + + rc = 4 # in Angstrom + ethresh = 1e-4 + + n_atoms = len(serials) + + atomTemplate, residueTemplate = read_xml(xml) + atomDicts, residueDicts = init_residues(serials, names, resNames, resSeqs, positions, charges, atomTemplate, residueTemplate) + + Q = np.vstack( + [(atom.c0, atom.dX*10, atom.dY*10, atom.dZ*10, atom.qXX*300, atom.qYY*300, atom.qZZ*300, atom.qXY*300, atom.qXZ*300, atom.qYZ*300) for atom in atomDicts.values()] + ) + Q = jnp.array(Q) + Q_local = convert_cart2harm(Q, 2) + axis_type = np.array( + [atom.axisType for atom in atomDicts.values()] + ) + axis_indices = np.vstack( + [atom.axis_indices for atom in atomDicts.values()] + ) + covalent_map = assemble_covalent(residueDicts, n_atoms) + + + displacement_fn, shift_fn = space.periodic_general(box, fractional_coordinates=False) + neighbor_list_fn = partition.neighbor_list(displacement_fn, box, rc, 0, format=partition.OrderedSparse) + nbr = neighbor_list_fn.allocate(positions) + pairs = nbr.idx.T + # pairs = pairs[pairs[:, 0] < pairs[:, 1]] + + lmax = 2 + + + # Finish data preparation + # ------------------------------------------------------------------------------------- + # kappa, K1, K2, K3 = setup_ewald_parameters(rc, ethresh, box) + # # for debugging + # kappa = 0.657065221219616 + # construct_local_frames_fn = generate_construct_local_frames(axis_type, axis_indices) + # energy_force_pme = value_and_grad(energy_pme) + # e, f = energy_force_pme(positions, box, pairs, Q_local, mScales, pScales, dScales, covalent_map, construct_local_frames_fn, kappa, K1, K2, K3, lmax) + # print('ok') + # e, f = energy_force_pme(positions, box, pairs, Q_local, mScales, pScales, dScales, covalent_map, construct_local_frames_fn, kappa, K1, K2, K3, lmax) + # print(e) + + pme_force = ADMPPmeForce(box, axis_type, axis_indices, covalent_map, rc, ethresh, lmax) + pme_force.update_env('kappa', 0.657065221219616) + + E, F = pme_force.get_forces(positions, box, pairs, Q_local, mScales) + print('ok') + E, F = pme_force.get_forces(positions, box, pairs, Q_local, mScales) + print(E) + return + + +# below is the validation code +if __name__ == '__main__': + validation(sys.argv[1]) diff --git a/dmff/admp/recip.py b/dmff/admp/recip.py new file mode 100755 index 000000000..d0932586f --- /dev/null +++ b/dmff/admp/recip.py @@ -0,0 +1,550 @@ + +import numpy as np +import jax.numpy as jnp +import jax.scipy as jsp +from jax import jit +from dmff.settings import DO_JIT +from dmff.admp.pme import DIELECTRIC + +# for debug +# from admp.parser import * +# from admp.multipole import * +# from admp.spatial import * +# from admp.pme import * +# from jax.config import config +# config.update("jax_enable_x64", True) + +sqrt_pi = 1.7724538509055159 + +def generate_pme_recip(Ck_fn, kappa, gamma, pme_order, K1, K2, K3, lmax): + + # Currently only supports pme_order=6 + # Because only the 6-th order spline function is hard implemented + pme_order = 6 + # global variables for the reciprocal module, all related to pme_order + bspline_range = jnp.arange(-pme_order//2, pme_order//2) + n_mesh = pme_order**3 + shifts = jnp.array(jnp.meshgrid(bspline_range, bspline_range, bspline_range)).T.reshape((1, n_mesh, 3)) + + def pme_recip(positions, box, Q): + ''' + The generated pme_recip space calculator + kappa, pme_order, K1, K2, K3, and lmax are passed and fixed when the calculator is generated + ''' + + def get_recip_vectors(N, box): + """ + Computes reciprocal lattice vectors of the grid + + Input: + N: + (3,)-shaped array, (K1, K2, K3) + box: + 3 x 3 matrix, box parallelepiped vectors arranged in TODO rows or columns? + + Output: + Nj_Aji_star: + 3 x 3 matrix, the first index denotes reciprocal lattice vector, the second index is the component xyz. + (lattice vectors arranged in rows) + """ + Nj_Aji_star = (N.reshape((1, 3)) * jnp.linalg.inv(box)).T + return Nj_Aji_star + + + def u_reference(R_a, Nj_Aji_star): + """ + Each atom is meshed to dispersion_ORDER**3 points on the m-meshgrid. + This function computes the xyz-index of the reference point, which is the point on the meshgrid just above atomic coordinates, + and the corresponding values of xyz fractional displacements from real coordinate to the reference point. + + Inputs: + R_a: + N_a * 3 matrix containing positions of sites + Nj_Aji_star: + 3 x 3 matrix, the first index denotes reciprocal lattice vector, the second index is the component xyz. + (lattice vectors arranged in rows) + + Outputs: + m_u0: + N_a * 3 matrix, positions of the reference points of R_a on the m-meshgrid + u0: + N_a * 3 matrix, (R_a - R_m)*a_star values + """ + R_in_m_basis = jnp.einsum("ij,kj->ki", Nj_Aji_star, R_a) + m_u0 = jnp.ceil(R_in_m_basis).astype(int) + u0 = (m_u0 - R_in_m_basis) + pme_order/2 + return m_u0, u0 + + def bspline(u, order=pme_order): + """ + Computes the cardinal B-spline function + """ + if order == 6: + return jnp.piecewise(u, + [jnp.logical_and(u>=0, u<1.), + jnp.logical_and(u>=1, u<2.), + jnp.logical_and(u>=2, u<3.), + jnp.logical_and(u>=3, u<4.), + jnp.logical_and(u>=4, u<5.), + jnp.logical_and(u>=5, u<6.)], + [lambda u: u**5/120, + lambda u: u**5/120 - (u - 1)**5/20, + lambda u: u**5/120 + (u - 2)**5/8 - (u - 1)**5/20, + lambda u: u**5/120 - (u - 3)**5/6 + (u - 2)**5/8 - (u - 1)**5/20, + lambda u: u**5/24 - u**4 + 19*u**3/2 - 89*u**2/2 + 409*u/4 - 1829/20, + lambda u: -u**5/120 + u**4/4 - 3*u**3 + 18*u**2 - 54*u + 324/5] ) + + + def bspline_prime(u, order=pme_order): + """ + Computes first derivative of the cardinal B-spline function + """ + if order == 6: + return jnp.piecewise(u, + [jnp.logical_and(u>=0., u<1.), + jnp.logical_and(u>=1., u<2.), + jnp.logical_and(u>=2., u<3.), + jnp.logical_and(u>=3., u<4.), + jnp.logical_and(u>=4., u<5.), + jnp.logical_and(u>=5., u<6.)], + [lambda u: u**4/24, + lambda u: u**4/24 - (u - 1)**4/4, + lambda u: u**4/24 + 5*(u - 2)**4/8 - (u - 1)**4/4, + lambda u: -5*u**4/12 + 6*u**3 - 63*u**2/2 + 71*u - 231/4, + lambda u: 5*u**4/24 - 4*u**3 + 57*u**2/2 - 89*u + 409/4, + lambda u: -u**4/24 + u**3 - 9*u**2 + 36*u - 54] ) + + + def bspline_prime2(u, order=pme_order): + """ + Computes second derivate of the cardinal B-spline function + """ + if order == 6: + return jnp.piecewise(u, + [jnp.logical_and(u>=0., u<1.), + jnp.logical_and(u>=1., u<2.), + jnp.logical_and(u>=2., u<3.), + jnp.logical_and(u>=3., u<4.), + jnp.logical_and(u>=4., u<5.), + jnp.logical_and(u>=5., u<6.)], + [lambda u: u**3/6, + lambda u: u**3/6 - (u - 1)**3, + lambda u: 5*u**3/3 - 12*u**2 + 27*u - 19, + lambda u: -5*u**3/3 + 18*u**2 - 63*u + 71, + lambda u: 5*u**3/6 - 12*u**2 + 57*u - 89, + lambda u: -u**3/6 + 3*u**2 - 18*u + 36,] ) + + + def theta_eval(u, M_u): + """ + Evaluates the value of theta given 3D u values at ... points + + Input: + u: + ... x 3 matrix + + Output: + theta: + ... matrix + """ + theta = jnp.prod(M_u, axis = -1) + return theta + + + def thetaprime_eval(u, Nj_Aji_star, M_u, Mprime_u): + """ + First derivative of theta with respect to x,y,z directions + + Input: + u + Nj_Aji_star: + reciprocal lattice vectors + + Output: + N_a * 3 matrix + """ + + div = jnp.array([ + Mprime_u[:, 0] * M_u[:, 1] * M_u[:, 2], + Mprime_u[:, 1] * M_u[:, 2] * M_u[:, 0], + Mprime_u[:, 2] * M_u[:, 0] * M_u[:, 1], + ]).T + + # Notice that u = m_u0 - R_in_m_basis + 6/2 + # therefore the Jacobian du_j/dx_i = - Nj_Aji_star + return jnp.einsum("ij,kj->ki", -Nj_Aji_star, div) + + + def theta2prime_eval(u, Nj_Aji_star, M_u, Mprime_u, M2prime_u): + """ + compute the 3 x 3 second derivatives of theta with respect to xyz + + Input: + u + Nj_Aji_star + + Output: + N_A * 3 * 3 + """ + + div_00 = M2prime_u[:, 0] * M_u[:, 1] * M_u[:, 2] + div_11 = M2prime_u[:, 1] * M_u[:, 0] * M_u[:, 2] + div_22 = M2prime_u[:, 2] * M_u[:, 0] * M_u[:, 1] + + div_01 = Mprime_u[:, 0] * Mprime_u[:, 1] * M_u[:, 2] + div_02 = Mprime_u[:, 0] * Mprime_u[:, 2] * M_u[:, 1] + div_12 = Mprime_u[:, 1] * Mprime_u[:, 2] * M_u[:, 0] + + div_10 = div_01 + div_20 = div_02 + div_21 = div_12 + + div = jnp.array([ + [div_00, div_01, div_02], + [div_10, div_11, div_12], + [div_20, div_21, div_22], + ]).swapaxes(0, 2) + + # Notice that u = m_u0 - R_in_m_basis + 6/2 + # therefore the Jacobian du_j/dx_i = - Nj_Aji_star + return jnp.einsum("im,jn,kmn->kij", -Nj_Aji_star, -Nj_Aji_star, div) + + + def sph_harmonics_GO(u0, Nj_Aji_star): + ''' + Find out the value of spherical harmonics GRADIENT OPERATORS, assume the order is: + 00, 10, 11c, 11s, 20, 21c, 21s, 22c, 22s, ... + Currently supports lmax <= 2 + + Inputs: + u0: + a N_a * 3 matrix containing all positions + Nj_Aji_star: + reciprocal lattice vectors in the m-grid + lmax: + int: max L + + Output: + harmonics: + a Na * (6**3) * (l+1)^2 matrix, STGO operated on theta, + evaluated at 6*6*6 integer points about reference points m_u0 + ''' + + n_harm = int((lmax + 1)**2) + + N_a = u0.shape[0] + # mesh points around each site + u = (u0[:, jnp.newaxis, :] + shifts).reshape((N_a*n_mesh, 3)) + + M_u = bspline(u) + theta = theta_eval(u, M_u) + if lmax == 0: + return theta.reshape(N_a, n_mesh, n_harm) + + # dipole + Mprime_u = bspline_prime(u) + thetaprime = thetaprime_eval(u, Nj_Aji_star, M_u, Mprime_u) + harmonics_1 = jnp.stack( + [theta, + thetaprime[:, 2], + thetaprime[:, 0], + thetaprime[:, 1]], + axis = -1 + ) + + if lmax == 1: + return harmonics_1.reshape(N_a, n_mesh, n_harm) + + # quadrapole + M2prime_u = bspline_prime2(u) + theta2prime = theta2prime_eval(u, Nj_Aji_star, M_u, Mprime_u, M2prime_u) + rt3 = jnp.sqrt(3) + harmonics_2 = jnp.hstack( + [harmonics_1, + jnp.stack([(3*theta2prime[:,2,2] - jnp.trace(theta2prime, axis1=1, axis2=2)) / 2, + rt3 * theta2prime[:, 0, 2], + rt3 * theta2prime[:, 1, 2], + rt3/2 * (theta2prime[:, 0, 0] - theta2prime[:, 1, 1]), + rt3 * theta2prime[:, 0, 1]], axis = 1)] + ) + if lmax == 2: + return harmonics_2.reshape(N_a, n_mesh, n_harm) + else: + raise NotImplementedError('l > 2 (beyond quadrupole) not supported') + + + def Q_m_peratom(Q, sph_harms): + """ + Computes . See eq. (49) of https://doi.org/10.1021/ct5007983 + + Inputs: + Q: + N_a * (l+1)**2 matrix containing global frame multipole moments up to lmax, + sph_harms: + N_a, 216, (l+1)**2 + lmax: + int: maximal L + + Output: + Q_m_pera: + N_a * 216 matrix, values of theta evaluated on a 6 * 6 block about the atoms + """ + + N_a = sph_harms.shape[0] + + if lmax > 2: + raise NotImplementedError('l > 2 (beyond quadrupole) not supported') + + Q_dbf = Q[:, 0:1] + + if lmax >= 1: + Q_dbf = jnp.hstack([Q_dbf, Q[:,1:4]]) + if lmax >= 2: + Q_dbf = jnp.hstack([Q_dbf, Q[:,4:9]/3]) + + Q_m_pera = jnp.sum(Q_dbf[:,jnp.newaxis,:]* sph_harms, axis=2) + + assert Q_m_pera.shape == (N_a, n_mesh) + return Q_m_pera + + + def Q_mesh_on_m(Q_mesh_pera, m_u0, N): + """ + Reduce the local Q_m_peratom into the global mesh + + Input: + Q_mesh_pera, m_u0, N + + Output: + Q_mesh: + Nx * Ny * Nz matrix + """ + indices_arr = jnp.mod(m_u0[:,np.newaxis,:]+shifts, N[np.newaxis, np.newaxis, :]) + ### jax trick implementation without using for loop + ### NOTICE: this implementation does not work with numpy! + Q_mesh = jnp.zeros((N[0], N[1], N[2])) + Q_mesh = Q_mesh.at[indices_arr[:, :, 0], indices_arr[:, :, 1], indices_arr[:, :, 2]].add(Q_mesh_pera) + return Q_mesh + + + def setup_kpts_integer(N): + """ + Outputs: + kpts_int: + n_k * 3 matrix, n_k = N[0] * N[1] * N[2] + """ + N_half = N.reshape(3) + kx, ky, kz = [jnp.roll(jnp.arange(- (N_half[i] - 1) // 2, (N_half[i] + 1) // 2 ), - (N_half[i] - 1) // 2) for i in range(3)] + kpts_int = jnp.hstack([ki.flatten()[:,jnp.newaxis] for ki in jnp.meshgrid(kz, kx, ky)]) + return kpts_int + + + def setup_kpts(box, kpts_int): + ''' + This function sets up the k-points used for reciprocal space calculations + + Input: + box: + 3 * 3, three axis arranged in rows + kpts_int: + n_k * 3 matrix + + Output: + kpts: + 4 * K, K=K1*K2*K3, contains kx, ky, kz, k^2 for each kpoint + ''' + # in this array, a*, b*, c* (without 2*pi) are arranged in column + box_inv = jnp.linalg.inv(box) + # K * 3, coordinate in reciprocal space + kpts = 2 * jnp.pi * kpts_int.dot(box_inv) + ksq = jnp.sum(kpts**2, axis=1) + # 4 * K + kpts = jnp.hstack((kpts, ksq[:, jnp.newaxis])).T + return kpts + + + def spread_Q(positions, box, Q): + ''' + This is the high level wrapper function, in charge of spreading the charges/multipoles on grid + + Input: + positions: + Na * 3: positions of each site + box: + 3 * 3: box + Q: + Na * (lmax+1)**2: the multipole of each site in global frame + + Output: + Q_mesh: + K1 * K2 * K3: the meshed multipoles + + ''' + Nj_Aji_star = get_recip_vectors(N, box) + # For each atom, find the reference mesh point, and u position of the site + m_u0, u0 = u_reference(positions, Nj_Aji_star) + # find out the STGO values of each grid point + sph_harms = sph_harmonics_GO(u0, Nj_Aji_star) + # find out the local meshed values for each site + Q_mesh_pera = Q_m_peratom(Q, sph_harms) + return Q_mesh_on_m(Q_mesh_pera, m_u0, N) + + # spread Q + N = np.array([K1, K2, K3]) + Q_mesh = spread_Q(positions, box, Q) + N = N.reshape(1, 1, 3) + kpts_int = setup_kpts_integer(N) + kpts = setup_kpts(box, kpts_int) + m = jnp.linspace(-pme_order//2+1, pme_order//2-1, pme_order-1).reshape(pme_order-1, 1, 1) + # m = jnp.linspace(-2,2,5).reshape(5, 1, 1) + theta_k = jnp.prod( + jnp.sum( + bspline(m + pme_order/2) * jnp.cos(2*jnp.pi*m*kpts_int[jnp.newaxis] / N), + axis = 0 + ), + axis = 1 + ) + V = jnp.linalg.det(box) + S_k = jnp.fft.fftn(Q_mesh).flatten() + # for electrostatic, need to exclude gamma point + # for dispersion, need to include gamma point + if not gamma: + C_k = Ck_fn(kpts[3, 1:], kappa, V) + E_k = C_k * jnp.abs(S_k[1:] / theta_k[1:])**2 + else: + C_k = Ck_fn(kpts[3, :], kappa, V) + # debug + # for i in range(1000): + # print('%15.8f%15.8f'%(jnp.real(C_k[i]), jnp.imag(C_k[i]))) + E_k = C_k * jnp.abs(S_k / theta_k)**2 + + if not gamma: # doing electrics + return jnp.sum(E_k) * DIELECTRIC + else: + return jnp.sum(E_k) + + if DO_JIT: + return jit(pme_recip, static_argnums=()) + else: + return pme_recip + + +def Ck_1(ksq, kappa, V): + return 2*jnp.pi/V/ksq * jnp.exp(-ksq/4/kappa**2) + +def Ck_6(ksq, kappa, V): + x2 = ksq / 4 / kappa**2 + x = jnp.sqrt(x2) + x3 = x2 * x + exp_x2 = jnp.exp(-x2) + f = (1 - 2*x2)*exp_x2 + 2*x3*sqrt_pi*jsp.special.erfc(x) + return sqrt_pi*jnp.pi/2/V*kappa**3 * f / 3 + +def Ck_8(ksq, kappa, V): + x2 = ksq / 4 / kappa**2 + x = jnp.sqrt(x2) + x4 = x2 * x2 + x5 = x4 * x + exp_x2 = jnp.exp(-x2) + f = (3 - 2*x2 + 4*x4)*exp_x2 - 4*x5*sqrt_pi*jsp.special.erfc(x) + return sqrt_pi*jnp.pi/2/V*kappa**5 * f / 45 + +def Ck_10(ksq, kappa, V): + x2 = ksq / 4 / kappa**2 + x = jnp.sqrt(x2) + x4 = x2 * x2 + x6 = x4 * x2 + x7 = x6 * x + exp_x2 = jnp.exp(-x2) + f = (15 - 6*x2 + 4*x4 - 8*x6)*exp_x2 + 8*x7*sqrt_pi*jsp.special.erfc(x) + return sqrt_pi*jnp.pi/2/V*kappa**7 * f / 1260 + + +# def validation(pdb): +# jnp.set_printoptions(precision=32, suppress=True) +# xml = 'mpidwater.xml' +# pdbinfo = read_pdb(pdb) +# serials = pdbinfo['serials'] +# names = pdbinfo['names'] +# resNames = pdbinfo['resNames'] +# resSeqs = pdbinfo['resSeqs'] +# positions = pdbinfo['positions'] +# box = pdbinfo['box'] # a, b, c, α, β, γ +# charges = pdbinfo['charges'] +# positions = jnp.asarray(positions) +# lx, ly, lz, _, _, _ = box +# box = jnp.eye(3)*jnp.array([lx, ly, lz]) + +# rc = 4 # in Angstrom +# ethresh = 1e-4 + +# n_atoms = len(serials) + +# atomTemplate, residueTemplate = read_xml(xml) +# atomDicts, residueDicts = init_residues(serials, names, resNames, resSeqs, positions, charges, atomTemplate, residueTemplate) + +# Q = np.vstack( +# [(atom.c0, atom.dX*10, atom.dY*10, atom.dZ*10, atom.qXX*300, atom.qYY*300, atom.qZZ*300, atom.qXY*300, atom.qXZ*300, atom.qYZ*300) for atom in atomDicts.values()] +# ) +# Q = jnp.array(Q) +# Q_local = convert_cart2harm(Q, 2) +# axis_type = np.array( +# [atom.axisType for atom in atomDicts.values()] +# ) +# axis_indices = np.vstack( +# [atom.axis_indices for atom in atomDicts.values()] +# ) +# covalent_map = assemble_covalent(residueDicts, n_atoms) + + +# # invoke pme energy calculator +# # energy_pme(positions, box, Q_local, axis_type, axis_indices, nbr.idx, 2) +# lmax = 2 +# kappa, K1, K2, K3 = setup_ewald_parameters(rc, ethresh, box) +# # for debugging +# kappa = 0.657065221219616 +# construct_local_frames_fn = generate_construct_local_frames(axis_type, axis_indices) +# local_frames = construct_local_frames_fn(positions, box) +# Q_global = rot_local2global(Q_local, local_frames, lmax) + +# pme_order = 6 +# energy_force_pme_recip = value_and_grad(generate_pme_recip(Ck_1, kappa, False, pme_order, K1, K2, K3, lmax)) +# energy_force_pme_recip(positions, box, Q_global) +# print('ok') +# E, F = energy_force_pme_recip(positions, box, Q_global) +# print(E) + +# # construct the C list +# c_list = np.zeros((3,n_atoms)) +# nmol=int(n_atoms/3) +# for i in range(nmol): +# a = i*3 +# b = i*3+1 +# c = i*3+2 +# c_list[0][a]=37.19677405 +# c_list[0][b]=7.6111103 +# c_list[0][c]=7.6111103 +# c_list[1][a]=85.26810658 +# c_list[1][b]=11.90220148 +# c_list[1][c]=11.90220148 +# c_list[2][a]=134.44874488 +# c_list[2][b]=15.05074749 +# c_list[2][c]=15.05074749 +# energy_force_d6_recip = value_and_grad(generate_pme_recip(Ck_6, kappa, True, pme_order, K1, K2, K3, 0)) +# energy_force_d8_recip = value_and_grad(generate_pme_recip(Ck_8, kappa, True, pme_order, K1, K2, K3, 0)) +# energy_force_d10_recip = value_and_grad(generate_pme_recip(Ck_10, kappa, True, pme_order, K1, K2, K3, 0)) +# E6, F6 = energy_force_d6_recip(positions, box, c_list[0, :, jnp.newaxis]) +# E8, F6 = energy_force_d8_recip(positions, box, c_list[1, :, jnp.newaxis]) +# E10, F10 = energy_force_d10_recip(positions, box, c_list[2, :, jnp.newaxis]) +# print('ok') +# E6, F6 = energy_force_d6_recip(positions, box, c_list[0, :, jnp.newaxis]) +# E8, F6 = energy_force_d8_recip(positions, box, c_list[1, :, jnp.newaxis]) +# E10, F10 = energy_force_d10_recip(positions, box, c_list[2, :, jnp.newaxis]) +# print(E6, E8, E10) +# print(E6 + E8 + E10) +# return + + +# # validation code +# if __name__ == '__main__': +# validation(sys.argv[1]) diff --git a/dmff/admp/settings.py b/dmff/admp/settings.py new file mode 100644 index 000000000..f08e88162 --- /dev/null +++ b/dmff/admp/settings.py @@ -0,0 +1,3 @@ +# DEFAULT THRESHOLDS +POL_CONV = 10.0 # gradient convergence thresh for induced dipoles +MAX_N_POL = 30 # maximum number of cyles for optimizing induced dipole \ No newline at end of file diff --git a/dmff/admp/spatial.py b/dmff/admp/spatial.py new file mode 100644 index 000000000..caceb9f15 --- /dev/null +++ b/dmff/admp/spatial.py @@ -0,0 +1,179 @@ + +import jax.numpy as jnp +from jax import vmap, jit +import numpy as np +from functools import partial +from dmff.settings import DO_JIT +from dmff.utils import jit_condition + +# This module deals with spatial geometric operations, mainly including: +# 1. PBC related operations +# 2. Local frame rotation operations + +@jit_condition(static_argnums=()) +def pbc_shift(drvecs, box, box_inv): + ''' + Dealing with the pbc shifts of vectors + + Inputs: + rvecs: + N * 3, a list of real space vectors in Cartesian + box: + 3 * 3, box matrix, with axes arranged in rows + box_inv: + 3 * 3, inverse of box matrix + + Outputs: + rvecs: + N * 3, vectors that have been shifted, in Cartesian + ''' + unshifted_dsvecs = drvecs.dot(box_inv) + dsvecs = unshifted_dsvecs - jnp.floor(unshifted_dsvecs + 0.5) + return dsvecs.dot(box) + +v_pbc_shift = vmap(pbc_shift, in_axes=(0, None, None), out_axes=0) + +def normalize(matrix, axis=1, ord=2): + ''' + Normalise a matrix along one dimension + ''' + normalised = matrix / jnp.linalg.norm(matrix, axis=axis, keepdims=True, ord=ord) + return normalised + + +def generate_construct_local_frames(axis_types, axis_indices): + """ + Generates the local frame constructor, common to the same physical system + + inputs: + axis_types: + N, a len(N) integer array, labels the types of localframe transformation rules for each atom. + axis_indices: + N * 3, indices of z,x,y atoms of the localframe of each atom. + + outputs: + construct_local_frames: + function type (positions, box) -> local_frames + """ + ZThenX = 0 + Bisector = 1 + ZBisect = 2 + ThreeFold = 3 + Zonly = 4 + NoAxisType = 5 + LastAxisTypeIndex = 6 + + z_atoms = jnp.array(axis_indices[:, 0]) + x_atoms = jnp.array(axis_indices[:, 1]) + y_atoms = jnp.array(axis_indices[:, 2]) + + Zonly_filter = (axis_types == Zonly) + not_Zonly_filter = jnp.logical_not(Zonly_filter) + Bisector_filter = (axis_types == Bisector) + ZBisect_filter = (axis_types == ZBisect) + ThreeFold_filter = (axis_types == ThreeFold) + + def construct_local_frames(positions, box): + ''' + This function constructs the local frames for each site + + Inputs: + positions: + N * 3: the positions matrix + box: + Outputs: + #jichen: + #NOTE: It doesn't seem to return Q + Q: + N*(lmax+1)^2, the multipole moments in global harmonics. + local_frames: + N*3*3, the local frames, axes arranged in rows + ''' + + positions = jnp.array(positions) + n_sites = positions.shape[0] + box_inv = jnp.linalg.inv(box) + + ### Process the x, y, z vectors according to local axis rules + vec_z = pbc_shift(positions[z_atoms] - positions, box, box_inv) + vec_z = normalize(vec_z) + vec_x = jnp.zeros((n_sites, 3)) + vec_y = jnp.zeros((n_sites, 3)) + # Z-Only + x_of_vec_z = jnp.round(jnp.abs(vec_z[:,0])) + vec_x_Zonly = jnp.array([1.-x_of_vec_z, x_of_vec_z, jnp.zeros_like(x_of_vec_z)]).T + vec_x = vec_x.at[Zonly_filter].set(vec_x_Zonly) + # for those that are not Z-Only, get normalized vecX + vec_x_not_Zonly = positions[x_atoms[not_Zonly_filter]] - positions[not_Zonly_filter] + vec_x_not_Zonly = pbc_shift(vec_x_not_Zonly, box, box_inv) + + vec_x = vec_x.at[not_Zonly_filter].set(normalize(vec_x_not_Zonly, axis=1)) + # Bisector + if np.sum(Bisector_filter) > 0: + vec_z_Bisector = vec_z[Bisector_filter] + vec_x[Bisector_filter] + vec_z = vec_z.at[Bisector_filter].set(normalize(vec_z_Bisector, axis=1)) + # z-bisector + if np.sum(ZBisect_filter) > 0: + vec_y_ZBisect = positions[y_atoms[ZBisect_filter]] - positions[ZBisect_filter] + vec_y_ZBisect = pbc_shift(vec_y_ZBisect, box, box_inv) + vec_y_ZBisect = normalize(vec_y_ZBisect, axis=1) + vec_x_ZBisect = vec_x[ZBisect_filter] + vec_y_ZBisect + vec_x = vec_x.at[ZBisect_filter].set(normalize(vec_x_ZBisect, axis=1)) + # ThreeFold + if np.sum(ThreeFold_filter) > 0: + vec_x_threeFold = vec_x[ThreeFold_filter] + vec_z_threeFold = vec_z[ThreeFold_filter] + + vec_y_threeFold = positions[y_atoms[ThreeFold_filter]] - positions[ThreeFold_filter] + vec_y_threeFold = pbc_shift(vec_y_threeFold, box, box_inv) + vec_y_threeFold = normalize(vec_y_threeFold, axis=1) + vec_z_threeFold += (vec_x_threeFold + vec_y_threeFold) + vec_z_threeFold = normalize(vec_z_threeFold) + + vec_y = vec_y.at[ThreeFold_filter].set(vec_y_threeFold) + vec_z = vec_z.at[ThreeFold_filter].set(vec_z_threeFold) + + # up to this point, z-axis should already be set up and normalized + xz_projection = jnp.sum(vec_x*vec_z, axis = 1, keepdims=True) + vec_x = normalize(vec_x - vec_z * xz_projection, axis=1) + # up to this point, x-axis should be ready + vec_y = jnp.cross(vec_z, vec_x) + + return jnp.stack((vec_x, vec_y, vec_z), axis=1) + + if DO_JIT: + return jit(construct_local_frames) + else: + return construct_local_frames + +@partial(vmap, in_axes=(0, 0, 0, 0), out_axes=0) +@jit_condition(static_argnums=()) +def build_quasi_internal(r1, r2, dr, norm_dr): + ''' + Build the quasi-internal frame between a pair of sites + In this frame, the z-axis is pointing from r2 to r1 + + Input: + r1: + N * 3, positions of the first vector + r2: + N * 3, positions of the second vector + dr: + N * 3, vector pointing from r1 to r2 + norm_dr: + (N,), distances between r1 and r2 + + Output: + local_frames: + N * 3 * 3: local frames, three axes arranged in rows + ''' + vectorZ = dr/norm_dr + # vectorX = jnp.where(jnp.logical_or(r1[:, 1] != r2[:, 1], r1[:, 2]!=r2[:, 2]).reshape((-1, 1)), vectorZ+jnp.array([1., 0., 0.]), vectorZ + jnp.array([0., 1., 0.])) + vectorX = jnp.where(jnp.logical_or(r1[1]!=r2[1], r1[2]!=r2[2]), vectorZ + jnp.array([1., 0., 0.]), vectorZ + jnp.array([0., 1., 0.])) + # dot = jnp.sum(vectorZ * vectorX, axis=1) + dot_xz = jnp.dot(vectorZ, vectorX) + vectorX -= vectorZ * dot_xz + vectorX = vectorX / jnp.linalg.norm(vectorX) + vectorY = jnp.cross(vectorZ, vectorX) + return jnp.stack([vectorX, vectorY, vectorZ]) + diff --git a/dmff/api.py b/dmff/api.py new file mode 100644 index 000000000..8d612ed51 --- /dev/null +++ b/dmff/api.py @@ -0,0 +1,514 @@ +#!/usr/bin/env python +import openmm.app as app +import openmm.unit as unit +import numpy as np +import jax.numpy as jnp +from collections import defaultdict +from .admp.disp_pme import ADMPDispPmeForce +from .admp.multipole import convert_cart2harm, rot_local2global +from .admp.pairwise import TT_damping_qq_c6_kernel, generate_pairwise_interaction +from .admp.pme import ADMPPmeForce +from .admp.spatial import generate_construct_local_frames +from .admp.recip import Ck_1, generate_pme_recip +from jax_md import space, partition +from jax import grad +import linecache + + + +def get_line_context(file_path, line_number): + return linecache.getline(file_path,line_number).strip() + + +def build_covalent_map(data, max_neighbor): + n_atoms = len(data.atoms) + covalent_map = np.zeros((n_atoms, n_atoms), dtype=int) + for bond in data.bonds: + covalent_map[bond.atom1, bond.atom2] = 1 + covalent_map[bond.atom2, bond.atom1] = 1 + for n_curr in range(1, max_neighbor): + for i in range(n_atoms): + # current neighbors + j_list = np.where( + np.logical_and(covalent_map[i] <= n_curr, covalent_map[i] > 0) + )[0] + for j in j_list: + k_list = np.where(covalent_map[j] == 1)[0] + for k in k_list: + if k != i and k not in j_list: + covalent_map[i, k] = n_curr + 1 + covalent_map[k, i] = n_curr + 1 + return covalent_map + + +def set_axis_type(map_atomtypes, types, params): + + ZThenX = 0 + Bisector = 1 + ZBisect = 2 + ThreeFold = 3 + Zonly = 4 + NoAxisType = 5 + LastAxisTypeIndex = 6 + kStrings = ["kz", "kx", "ky"] + axisIndices = [] + axisTypes = [] + + for i in map_atomtypes: + atomType = types[i] + + kIndices = [atomType] + + for kString in kStrings: + kString_value = params[kString][i] + if kString_value != "": + kIndices.append(kString_value) + axisIndices.append(kIndices) + + # set axis type + + kIndicesLen = len(kIndices) + + if kIndicesLen > 3: + ky = kIndices[3] + kyNegative = False + if ky.startswith("-"): + ky = kIndices[3] = ky[1:] + kyNegative = True + else: + ky = "" + + if kIndicesLen > 2: + kx = kIndices[2] + kxNegative = False + if kx.startswith("-"): + kx = kIndices[2] = kx[1:] + kxNegative = True + else: + kx = "" + + if kIndicesLen > 1: + kz = kIndices[1] + kzNegative = False + if kz.startswith("-"): + kz = kIndices[1] = kz[1:] + kzNegative = True + else: + kz = "" + + while len(kIndices) < 4: + kIndices.append("") + + axisType = ZThenX + if not kz: + axisType = NoAxisType + if kz and not kx: + axisType = Zonly + if kz and kzNegative or kx and kxNegative: + axisType = Bisector + if kx and kxNegative and ky and kyNegative: + axisType = ZBisect + if kz and kzNegative and kx and kxNegative and ky and kyNegative: + axisType = ThreeFold + + axisTypes.append(axisType) + + return np.array(axisTypes), np.array(axisIndices) + + +class ADMPDispGenerator: + def __init__(self, hamiltonian): + self.ff = hamiltonian + self.params = {"A": [], "B": [], "Q": [], "C6": [], "C8": [], "C10": []} + self._jaxPotential = None + self.types = [] + self.ethresh = 1.0e-5 + self.pmax = 10 + + def registerAtomType(self, atom): + self.types.append(atom["type"]) + self.params["A"].append(float(atom["A"])) + self.params["B"].append(float(atom["B"])) + self.params["Q"].append(float(atom["Q"])) + self.params["C6"].append(float(atom["C6"])) + self.params["C8"].append(float(atom["C8"])) + self.params["C10"].append(float(atom["C10"])) + + @staticmethod + def parseElement(element, hamiltonian): + generator = ADMPDispGenerator(hamiltonian) + hamiltonian.registerGenerator(generator) + # covalent scales + mScales = [] + for i in range(2, 7): + mScales.append(float(element.attrib["mScale1%d" % i])) + generator.params["mScales"] = mScales + for atomtype in element.findall("Atom"): + generator.registerAtomType(atomtype.attrib) + # jax it! + for k in generator.params.keys(): + generator.params[k] = jnp.array(generator.params[k]) + generator.types = np.array(generator.types) + + def createForce(self, system, data, nonbondedMethod, nonbondedCutoff, args): + + n_atoms = len(data.atoms) + # build index map + map_atomtype = np.zeros(n_atoms, dtype=int) + for i in range(n_atoms): + atype = data.atomType[data.atoms[i]] + map_atomtype[i] = np.where(self.types == atype)[0][0] + # build covalent map + covalent_map = build_covalent_map(data, 6) + + # here box is only used to setup ewald parameters, no need to be differentiable + a, b, c = system.getDefaultPeriodicBoxVectors() + box = jnp.array([a._value, b._value, c._value]) * 10 + # get the admp calculator + rc = nonbondedCutoff.value_in_unit(unit.angstrom) + + # get calculator + Force_DispPME = ADMPDispPmeForce(box, covalent_map, rc, self.ethresh, self.pmax) + # debugging + # Force_DispPME.update_env('kappa', 0.657065221219616) + # Force_DispPME.update_env('K1', 96) + # Force_DispPME.update_env('K2', 96) + # Force_DispPME.update_env('K3', 96) + pot_fn_lr = Force_DispPME.get_energy + pot_fn_sr = generate_pairwise_interaction( + TT_damping_qq_c6_kernel, covalent_map, static_args={} + ) + + def potential_fn(positions, box, pairs, params): + mScales = params["mScales"] + a_list = ( + params["A"][map_atomtype] / 2625.5 + ) # kj/mol to au, as expected by TT_damping kernel + b_list = params["B"][map_atomtype] * 0.0529177249 # nm^-1 to au + q_list = params["Q"][map_atomtype] + c6_list = jnp.sqrt(params["C6"][map_atomtype] * 1e6) + c8_list = jnp.sqrt(params["C8"][map_atomtype] * 1e8) + c10_list = jnp.sqrt(params["C10"][map_atomtype] * 1e10) + c_list = jnp.vstack((c6_list, c8_list, c10_list)) + + E_sr = pot_fn_sr( + positions, box, pairs, mScales, a_list, b_list, q_list, c_list[0] + ) + E_lr = pot_fn_lr(positions, box, pairs, c_list.T, mScales) + return E_sr - E_lr + + self._jaxPotential = potential_fn + # self._top_data = data + + def getJaxPotential(self): + return self._jaxPotential + + def renderXML(self): + # generate xml force field file + pass + + +# register all parsers +app.forcefield.parsers["ADMPDispForce"] = ADMPDispGenerator.parseElement + + +class ADMPPmeGenerator: + def __init__(self, hamiltonian): + self.ff = hamiltonian + self.kStrings = { + "kz": [], + "kx": [], + "ky": [], + } + self._input_params = { + "c0": [], + "dX": [], + "dY": [], + "dZ": [], + "qXX": [], + "qXY": [], + "qYY": [], + "qXZ": [], + "qYZ": [], + "qZZ": [], + "oXXX": [], + "oXXY": [], + "oXYY": [], + "oYYY": [], + "oXXZ": [], + "oXYZ": [], + "oYYZ": [], + "oXZZ": [], + "oYZZ": [], + "oZZZ": [], + "thole": [], + "polarizabilityXX": [], + "polarizabilityYY": [], + "polarizabilityZZ": [] + } + # if more or optional input params + # self._input_params = defaultDict(list) + self._jaxPotential = None + self.types = [] + self.ethresh = 1.0e-5 + self.params = {} + self.lpol = False + self.ref_dip = '' + + def registerAtomType(self, atom:dict): + + self.types.append(atom.pop("type")) + + kStrings = ["kz", "kx", "ky"] + for kString in kStrings: + if kString in atom: + self.kStrings[kString].append(atom.pop(kString)) + else: + self.kStrings[kString].append("") + + for k, v in atom.items(): + self._input_params[k].append(float(v)) + + @staticmethod + def parseElement(element, hamiltonian): + generator = ADMPPmeGenerator(hamiltonian) + generator.lmax = int(element.attrib.get('lmax')) + generator.pmax = int(element.attrib.get('pmax')) + + hamiltonian.registerGenerator(generator) + + mScales = [] + pScales = [] + dScales = [] + for i in range(2, 7): + mScales.append(float(element.attrib["mScale1%d" % i])) + pScales.append(float(element.attrib["pScale1%d" % i])) + dScales.append(float(element.attrib["dScale1%d" % i])) + generator.params["mScales"] = jnp.array(mScales) + generator.params["pScales"] = jnp.array(pScales) + generator.params["dScales"] = jnp.array(dScales) + + if element.findall('Polarize'): + generator.lpol = True + + for atomType in element.findall("Atom"): + atomAttrib = atomType.attrib + for polarInfo in element.findall("Polarize"): + polarAttrib = polarInfo.attrib + if polarInfo.attrib['type'] == atomAttrib['type']: + atomAttrib.update(polarAttrib) + break + generator.registerAtomType(atomAttrib) + + for k in generator._input_params.keys(): + generator._input_params[k] = jnp.array(generator._input_params[k]) + generator.types = np.array(generator.types) + + def createForce(self, system, data, nonbondedMethod, nonbondedCutoff, args): + + n_atoms = len(data.atoms) + # build index map + map_atomtype = np.zeros(n_atoms, dtype=int) + for i in range(n_atoms): + atype = data.atomType[data.atoms[i]] + map_atomtype[i] = np.where(self.types == atype)[0][0] + + # map atom multipole moments + p = self._input_params + Q = np.zeros((n_atoms, 10)) + Q[:, 0] = p["c0"][map_atomtype] + Q[:, 1] = p["dX"][map_atomtype] * 10 + Q[:, 2] = p["dY"][map_atomtype] * 10 + Q[:, 3] = p["dZ"][map_atomtype] * 10 + Q[:, 4] = p["qXX"][map_atomtype] * 300 + Q[:, 5] = p["qYY"][map_atomtype] * 300 + Q[:, 6] = p["qZZ"][map_atomtype] * 300 + Q[:, 7] = p["qXY"][map_atomtype] * 300 + Q[:, 8] = p["qXZ"][map_atomtype] * 300 + Q[:, 9] = p["qYZ"][map_atomtype] * 300 + + # map polarization-related params + pol = jnp.vstack((p['polarizabilityXX'][map_atomtype], p['polarizabilityYY'][map_atomtype], p['polarizabilityZZ'][map_atomtype])).T.astype(jnp.float32) + pol = 1000*jnp.mean(pol,axis=1) + self.params['pol'] = pol + + tholes = jnp.array(p['thole'][map_atomtype]).astype(jnp.float32) + tholes = jnp.mean(jnp.atleast_2d(tholes), axis=1) + self.params['tholes'] = tholes + + # defaultTholeWidth = 8 + Uind_global = jnp.zeros([n_atoms,3]) + ref_dip = self.ref_dip + for i in range(n_atoms): + a = get_line_context(ref_dip, i+1) + b = a.split() + t = np.array([10*float(b[0]),10*float(b[1]),10*float(b[2])]) + Uind_global = Uind_global.at[i].set(t) + + # construct the C list + c_list = np.zeros((3, n_atoms)) + a_list = np.zeros(n_atoms) + q_list = np.zeros(n_atoms) + b_list = np.zeros(n_atoms) + + + nmol=int(n_atoms/3) # WARNING: HARD CODE! + for i in range(nmol): + a = i*3 + b = i*3+1 + c = i*3+2 + # dispersion coeff + c_list[0][a]=37.19677405 + c_list[0][b]=7.6111103 + c_list[0][c]=7.6111103 + c_list[1][a]=85.26810658 + c_list[1][b]=11.90220148 + c_list[1][c]=11.90220148 + c_list[2][a]=134.44874488 + c_list[2][b]=15.05074749 + c_list[2][c]=15.05074749 + # q + q_list[a] = -0.741706 + q_list[b] = 0.370853 + q_list[c] = 0.370853 + # b, Bohr^-1 + b_list[a] = 2.00095977 + b_list[b] = 1.999519942 + b_list[c] = 1.999519942 + # a, Hartree + a_list[a] = 458.3777 + a_list[b] = 0.0317 + a_list[c] = 0.0317 + + # add all differentiable params to self.params + Q = jnp.array(Q) + Q_local = convert_cart2harm(Q, 2) + self.params["Q_local"] = Q_local + # here box is only used to setup ewald parameters, no need to be differentiable + a, b, c = system.getDefaultPeriodicBoxVectors() + box = jnp.array([a._value, b._value, c._value]) * 10 + + # get the admp calculator + rc = nonbondedCutoff.value_in_unit(unit.angstrom) + + # build covalent map + covalent_map = build_covalent_map(data, 6) + + # build intra-molecule axis + self.axis_types, self.axis_indices = set_axis_type( + map_atomtype, self.types, self.kStrings + ) + map_axis_indices = [] + # map axis_indices + for i in range(n_atoms): + catom = data.atoms[i] + residue = catom.residue._atoms + atom_indices = [ + index if index != "" else -1 for index in self.axis_indices[i][1:] + ] + for atom in residue: + if atom == catom: + continue + for i in range(len(atom_indices)): + if atom_indices[i] == data.atomType[atom]: + atom_indices[i] = atom.index + break + map_axis_indices.append(atom_indices) + + self.axis_indices = np.array(map_axis_indices) + + # Finish data preparation + # ------------------------------------------------------------------------------------- + # parameters should be ready: + # geometric variables: positions, box + # atomic parameters: Q_local, c_list + # topological parameters: covalent_map, mScales, pScales, dScales + # general force field setting parameters: rc, ethresh, lmax, pmax + + pme_force = ADMPPmeForce( + box, + self.axis_types, + self.axis_indices, + covalent_map, + rc, + self.ethresh, + self.lmax, + self.lpol + ) + self.params['U_ind'] = pme_force.U_ind + + + def potential_fn(positions, box, pairs, params): + + mScales = params["mScales"] + Q_local = params["Q_local"] + + + # positions, box, pairs, Q_local, mScales + if self.lpol: + pScales = params["pScales"] + dScales = params["dScales"] + U_ind = params["U_ind"] + return pme_force.get_energy(positions, box, pairs, Q_local, pol, tholes, mScales, pScales, dScales, U_init=U_ind) + else: + return pme_force.get_energy(positions, box, pairs, Q_local, mScales) + + self._jaxPotential = potential_fn + + def getJaxPotential(self): + return self._jaxPotential + + def renderXML(self): + pass + + +app.forcefield.parsers["ADMPPmeForce"] = ADMPPmeGenerator.parseElement + + +class Hamiltonian(app.forcefield.ForceField): + def __init__(self, xmlname): + super().__init__(xmlname) + self._potentials = [] + + def createPotential( + self, + topology, + nonbondedMethod=app.NoCutoff, + nonbondedCutoff=1.0 * unit.nanometer, + ): + system = self.createSystem( + topology, nonbondedMethod=nonbondedMethod, nonbondedCutoff=nonbondedCutoff + ) + # load_constraints_from_system_if_needed + # create potentials + for generator in self._forces: + potentialImpl = generator.getJaxPotential() + self._potentials.append(potentialImpl) + return [p for p in self._potentials] + + +if __name__ == "__main__": + H = Hamiltonian("forcefield.xml") + generator = H.getGenerators()[0] + app.Topology.loadBondDefinitions("residues.xml") + pdb = app.PDBFile("../water1024.pdb") + rc = 4.0 + potentials = H.createPotential(pdb.topology, nonbondedCutoff=rc * unit.angstrom) + pot_disp = potentials[0] + + positions = jnp.array(pdb.positions._value) * 10 + a, b, c = pdb.topology.getPeriodicBoxVectors() + box = jnp.array([a._value, b._value, c._value]) * 10 + + # neighbor list + displacement_fn, shift_fn = space.periodic_general( + box, fractional_coordinates=False + ) + neighbor_list_fn = partition.neighbor_list( + displacement_fn, box, rc, 0, format=partition.OrderedSparse + ) + nbr = neighbor_list_fn.allocate(positions) + pairs = nbr.idx.T + + param_grad = grad(pot_disp, argnums=3)(positions, box, pairs, generator.params) + print(param_grad) diff --git a/dmff/settings.py b/dmff/settings.py new file mode 100644 index 000000000..fa2e2bd92 --- /dev/null +++ b/dmff/settings.py @@ -0,0 +1,8 @@ +from jax.config import config + +PRECISION = 'double' # 'double' + +DO_JIT = True + +if PRECISION == 'double': + config.update("jax_enable_x64", True) diff --git a/dmff/utils.py b/dmff/utils.py new file mode 100644 index 000000000..6182b8123 --- /dev/null +++ b/dmff/utils.py @@ -0,0 +1,10 @@ +from dmff.settings import DO_JIT +from jax import jit + +def jit_condition(*args, **kwargs): + def jit_deco(func): + if DO_JIT: + return jit(func, *args, **kwargs) + else: + return func + return jit_deco \ No newline at end of file diff --git a/docs/.gitkeep b/docs/about.md similarity index 100% rename from docs/.gitkeep rename to docs/about.md diff --git a/docs/admp/README.md b/docs/admp/README.md new file mode 100644 index 000000000..9ca538f1a --- /dev/null +++ b/docs/admp/README.md @@ -0,0 +1,68 @@ +# ADMP + +Automatic Differentiable Multipolar Polarizable (ADMP) force field calculator. + +This module provides an auto-differentiable implementation of multipolar polarizable force fields, that resembles the behavior of [MPID](https://github.com/andysim/MPIDOpenMMPlugin) plugin of OpenMM. Supposedly, this module is developed for the following purposes: + +1. Achieving an easy calculation of force and virial of the multipolar polarizable forcefield. +2. Allowing fluctuating (geometric-dependent) multipoles/polarizabilities in multipolar polarizable potentials. +3. Allowing the calculation of derivatives of various force field parameters, thus achieving a more systematic and automatic parameter optimization scheme. + +The module is based on [JAX](https://github.com/google/jax) and [JAX-MD](https://github.com/google/jax-md) projects. + + + +## Installation + +### Dependencies + +ADMP module depends on the following packages, install them before using ADMP: + +1. Install [jax](https://github.com/google/jax) (pick the correct cuda version, see more details on their installation guide): + + ```bash + pip install jax[cuda11_cudnn82] -f https://storage.googleapis.com/jax-releases/jax_releases.html + ``` + +2. Install [jax-md](https://github.com/google/jax-md) : + + ```bash + pip install jax-md --upgrade + ``` + + ADMP currently relies on the space and partition modules to provide neighbor list + +3. Install ADMP: + + ADMP is a pure python module, just simply put it in your $PYTHONPATH. + + ```bash + export PYTHONPATH=$PYTHONPATH:/path/to/admp + ``` + + + +## Settings + +In `admp/settings.py`, you can modify some global settings, including: + +**PRECISION**: single or double precision + +**DO_JIT**: whether do jit or not. + + + +## Example + +We provide a MPID 1024 water box example. In water_1024 and water_pol_1024, we show both the nonpolarizable and the polarizable cases. + +```bash +cd ./examples/water_1024 +./run_admp.py + +cd ./examples/water_pol_1024 +./run_admp.py +``` + +if `DO_JIT = True`, then the first run would be a bit slow, since it tries to do the jit compilation. Further executions of `get_forces` or `get_energy` should be much faster. + diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 000000000..000ea3455 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,17 @@ +# Welcome to MkDocs + +For full documentation visit [mkdocs.org](https://www.mkdocs.org). + +## Commands + +* `mkdocs new [dir-name]` - Create a new project. +* `mkdocs serve` - Start the live-reloading docs server. +* `mkdocs build` - Build the documentation site. +* `mkdocs -h` - Print help message and exit. + +## Project layout + + mkdocs.yml # The configuration file. + docs/ + index.md # The documentation homepage. + ... # Other markdown pages, images and other files. diff --git a/docs/uder_guide/tutorial.md b/docs/uder_guide/tutorial.md new file mode 100644 index 000000000..e69de29bb diff --git a/examples/openmm_api/dipole_1024 b/examples/openmm_api/dipole_1024 new file mode 100644 index 000000000..e1de509e9 --- /dev/null +++ b/examples/openmm_api/dipole_1024 @@ 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b/examples/openmm_api/ref_out @@ -0,0 +1,3 @@ +166834.94 +[-8.7891670e+06 -1.6102563e+08 0.0000000e+00 0.0000000e+00 + 1.7047870e+05] diff --git a/examples/openmm_api/residues.xml b/examples/openmm_api/residues.xml new file mode 100644 index 000000000..3d4f36fb8 --- /dev/null +++ b/examples/openmm_api/residues.xml @@ -0,0 +1,6 @@ + + + + + + diff --git a/examples/openmm_api/run.py b/examples/openmm_api/run.py new file mode 100755 index 000000000..b8565fcf1 --- /dev/null +++ b/examples/openmm_api/run.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python +import sys +from pathlib import Path +admp_path = Path(__file__).parent.parent.parent +sys.path.append(str(admp_path)) +import openmm.app as app +import openmm.unit as unit +import jax.numpy as jnp +from dmff.api import Hamiltonian +from jax_md import space, partition +from jax import grad + + +if __name__ == '__main__': + + H = Hamiltonian('forcefield.xml') + app.Topology.loadBondDefinitions("residues.xml") + pdb = app.PDBFile("water1024.pdb") + rc = 4.0 + # generator stores all force field parameters + generator = H.getGenerators() + disp_generator = generator[0] + pme_generator = generator[1] + pme_generator.ref_dip = 'dipole_1024' + potentials = H.createPotential(pdb.topology, nonbondedCutoff=rc*unit.angstrom) + # pot_fn is the actual energy calculator + pot_disp = potentials[0] + pot_pme = potentials[1] + + # construct inputs + positions = jnp.array(pdb.positions._value) * 10 + a, b, c = pdb.topology.getPeriodicBoxVectors() + box = jnp.array([a._value, b._value, c._value]) * 10 + # neighbor list + displacement_fn, shift_fn = space.periodic_general(box, fractional_coordinates=False) + neighbor_list_fn = partition.neighbor_list(displacement_fn, box, rc, 0, format=partition.OrderedSparse) + nbr = neighbor_list_fn.allocate(positions) + pairs = nbr.idx.T + + print(pot_disp(positions, box, pairs, disp_generator.params)) + param_grad = grad(pot_disp, argnums=3)(positions, box, pairs, generator[0].params) + print(param_grad['mScales']) + + print(pot_pme(positions, box, pairs, pme_generator.params)) + param_grad = grad(pot_pme, argnums=3)(positions, box, pairs, generator[1].params) + print(param_grad['mScales']) + + diff --git a/examples/water_1024/mpidwater.xml b/examples/water_1024/mpidwater.xml new file mode 100644 index 000000000..4d0c677b8 --- /dev/null +++ b/examples/water_1024/mpidwater.xml @@ -0,0 +1,46 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/water_1024/ref_out b/examples/water_1024/ref_out new file mode 100644 index 000000000..d47bf8938 --- /dev/null +++ b/examples/water_1024/ref_out @@ -0,0 +1,6 @@ +Electrostatic Energy (kJ/mol) +-133.75 +Dispersion Energy (kJ/mol) +54660.043 +Tang-Tonnies Damping (kJ/mol) +221523.0 diff --git a/examples/water_1024/run_admp.py b/examples/water_1024/run_admp.py new file mode 100755 index 000000000..7bdb58204 --- /dev/null +++ b/examples/water_1024/run_admp.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python +import sys +from pathlib import Path +admp_path = Path(__file__).parent.parent.parent +sys.path.append(str(admp_path)) +import numpy as np +import jax.numpy as jnp +from jax import grad, value_and_grad +from jax_md import partition, space +from dmff.admp.multipole import convert_cart2harm +from dmff.admp.pme import ADMPPmeForce +from dmff.admp.disp_pme import ADMPDispPmeForce +from dmff.admp.pairwise import generate_pairwise_interaction, TT_damping_qq_c6_kernel +from dmff.admp.parser import * + + +# below is the validation code +if __name__ == '__main__': + pdb = 'water1024.pdb' + xml = 'mpidwater.xml' + pdbinfo = read_pdb(pdb) + serials = pdbinfo['serials'] + names = pdbinfo['names'] + resNames = pdbinfo['resNames'] + resSeqs = pdbinfo['resSeqs'] + positions = pdbinfo['positions'] + box = pdbinfo['box'] # a, b, c, α, β, γ + charges = pdbinfo['charges'] + positions = jnp.asarray(positions) + lx, ly, lz, _, _, _ = box + box = jnp.eye(3)*jnp.array([lx, ly, lz]) + + mScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + pScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + dScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + + rc = 4 # in Angstrom + ethresh = 1e-4 + + n_atoms = len(serials) + + atomTemplate, residueTemplate = read_xml(xml) + atomDicts, residueDicts = init_residues(serials, names, resNames, resSeqs, positions, charges, atomTemplate, residueTemplate) + + Q = np.vstack( + [(atom.c0, atom.dX*10, atom.dY*10, atom.dZ*10, atom.qXX*300, atom.qYY*300, atom.qZZ*300, atom.qXY*300, atom.qXZ*300, atom.qYZ*300) for atom in atomDicts.values()] + ) + Q = jnp.array(Q) + Q_local = convert_cart2harm(Q, 2) + axis_type = np.array( + [atom.axisType for atom in atomDicts.values()] + ) + axis_indices = np.vstack( + [atom.axis_indices for atom in atomDicts.values()] + ) + covalent_map = assemble_covalent(residueDicts, n_atoms) + + + lmax = 2 + pmax = 10 + + # construct the C list + c_list = np.zeros((3, n_atoms)) + a_list = np.zeros(n_atoms) + q_list = np.zeros(n_atoms) + b_list = np.zeros(n_atoms) + nmol=int(n_atoms/3) + for i in range(nmol): + a = i*3 + b = i*3+1 + c = i*3+2 + # dispersion coeff + c_list[0][a]=37.19677405 + c_list[0][b]=7.6111103 + c_list[0][c]=7.6111103 + c_list[1][a]=85.26810658 + c_list[1][b]=11.90220148 + c_list[1][c]=11.90220148 + c_list[2][a]=134.44874488 + c_list[2][b]=15.05074749 + c_list[2][c]=15.05074749 + # q + q_list[a] = -0.741706 + q_list[b] = 0.370853 + q_list[c] = 0.370853 + # b, Bohr^-1 + b_list[a] = 2.00095977 + b_list[b] = 1.999519942 + b_list[c] = 1.999519942 + # a, Hartree + a_list[a] = 458.3777 + a_list[b] = 0.0317 + a_list[c] = 0.0317 + + # Finish data preparation + # ------------------------------------------------------------------------------------- + # parameters should be ready: + # geometric variables: positions, box + # atomic parameters: Q_local, c_list + # topological parameters: covalent_map, mScales, pScales, dScales + # general force field setting parameters: rc, ethresh, lmax, pmax + + + # get neighbor list using jax_md + displacement_fn, shift_fn = space.periodic_general(box, fractional_coordinates=False) + neighbor_list_fn = partition.neighbor_list(displacement_fn, box, rc, 0, format=partition.OrderedSparse) + nbr = neighbor_list_fn.allocate(positions) + pairs = nbr.idx.T + + # electrostatic + pme_force = ADMPPmeForce(box, axis_type, axis_indices, covalent_map, rc, ethresh, lmax) + pme_force.update_env('kappa', 0.657065221219616) + E, F = pme_force.get_forces(positions, box, pairs, Q_local, mScales) + print('Electrostatic Energy (kJ/mol)') + # E = pme_force.get_energy(positions, box, pairs, Q_local, mScales, pScales, dScales) + E, F = pme_force.get_forces(positions, box, pairs, Q_local, mScales) + print(E) + + + # dispersion + disp_pme_force = ADMPDispPmeForce(box, covalent_map, rc, ethresh, pmax) + disp_pme_force.update_env('kappa', 0.657065221219616) + E, F = disp_pme_force.get_forces(positions, box, pairs, c_list.T, mScales) + print('Dispersion Energy (kJ/mol)') + # E = disp_pme_force.get_energy(positions, box, pairs, c_list.T, mScales) + E, F = disp_pme_force.get_forces(positions, box, pairs, c_list.T, mScales) + print(E) + + + # short range damping + TT_damping_qq_c6 = value_and_grad(generate_pairwise_interaction(TT_damping_qq_c6_kernel, covalent_map, static_args={})) + TT_damping_qq_c6(positions, box, pairs, mScales, a_list, b_list, q_list, c_list[0]) + print('Tang-Tonnies Damping (kJ/mol)') + E, F = TT_damping_qq_c6(positions, box, pairs, mScales, a_list, b_list, q_list, c_list[0]) + print(E) + diff --git a/examples/water_pol_1024/dipole_1024 b/examples/water_pol_1024/dipole_1024 new file mode 100644 index 000000000..e1de509e9 --- /dev/null +++ b/examples/water_pol_1024/dipole_1024 @@ -0,0 +1,3072 @@ +-9.78037e-05 -0.0102743 -0.00835381 +0 0 0 +0 0 0 +0.0016815 0.00104498 0.00955635 +0 0 0 +0 0 0 +0.0104807 0.0153637 -0.00934142 +0 0 0 +0 0 0 +0.00268184 0.00663563 -0.00085569 +0 0 0 +0 0 0 +0.000439397 0.0120955 -0.00798317 +0 0 0 +0 0 0 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a/examples/water_pol_1024/ref_out b/examples/water_pol_1024/ref_out new file mode 100644 index 000000000..91dc38b55 --- /dev/null +++ b/examples/water_pol_1024/ref_out @@ -0,0 +1,3073 @@ +# Electrostatic Energy (kJ/mol) +-0.000978037 -0.000978037 -0.0009246344 +-0.102743 -0.102743 -0.10276242 +-0.0835381 -0.0835381 -0.083497904 +0.016815 0.016815 0.016769728 +0.0104498 0.0104498 0.010405844 +0.0955635 0.0955635 0.09559388 +0.104807 0.104807 0.10483148 +0.153637 0.153637 0.15365247 +-0.0934142 -0.0934142 -0.09342755 +0.0268184 0.0268184 0.026805144 +0.0663563 0.0663563 0.06643988 +-0.0085569 -0.0085569 -0.008542149 +0.00439397 0.00439397 0.0043884227 +0.120955 0.120955 0.120904334 +-0.0798317 -0.0798317 -0.07980968 +0.0757021 0.0757021 0.07567475 +0.0274864 0.0274864 0.027549084 +-0.0405105 -0.0405105 -0.040603403 +-0.0374424 -0.0374424 -0.03742649 +-0.0805664 -0.0805664 -0.08054199 +0.0310999 0.0310999 0.031121153 +0.0300064 0.0300064 0.030048737 +0.0103427 0.0103427 0.010275945 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0.143451 0.14341389 +-0.101802 -0.101802 -0.10180697 diff --git a/examples/water_pol_1024/run_admp.py b/examples/water_pol_1024/run_admp.py new file mode 100755 index 000000000..65679e25f --- /dev/null +++ b/examples/water_pol_1024/run_admp.py @@ -0,0 +1,147 @@ +#!/usr/bin/env python +import sys +from pathlib import Path +admp_path = Path(__file__).parent.parent.parent +sys.path.append(str(admp_path)) +import numpy as np +import jax.numpy as jnp +from jax_md import partition, space +from dmff.admp.multipole import convert_cart2harm +from dmff.admp.pme import ADMPPmeForce +from dmff.admp.parser import * + + +import linecache +def get_line_context(file_path, line_number): + return linecache.getline(file_path,line_number).strip() + +# below is the validation code +if __name__ == '__main__': + pdb = str('water1024.pdb') + xml = str('mpidwater.xml') + ref_dip = str('dipole_1024') + pdbinfo = read_pdb(pdb) + serials = pdbinfo['serials'] + names = pdbinfo['names'] + resNames = pdbinfo['resNames'] + resSeqs = pdbinfo['resSeqs'] + positions = pdbinfo['positions'] + box = pdbinfo['box'] # a, b, c, α, β, γ + charges = pdbinfo['charges'] + positions = jnp.asarray(positions) + lx, ly, lz, _, _, _ = box + box = jnp.eye(3)*jnp.array([lx, ly, lz]) + + mScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + pScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + dScales = jnp.array([0.0, 0.0, 0.0, 1.0, 1.0]) + + rc = 4 # in Angstrom + ethresh = 1e-4 + + n_atoms = len(serials) + + atomTemplate, residueTemplate = read_xml(xml) + atomDicts, residueDicts = init_residues(serials, names, resNames, resSeqs, positions, charges, atomTemplate, residueTemplate) + + Q = np.vstack( + [(atom.c0, atom.dX*10, atom.dY*10, atom.dZ*10, atom.qXX*300, atom.qYY*300, atom.qZZ*300, atom.qXY*300, atom.qXZ*300, atom.qYZ*300) for atom in atomDicts.values()] + ) + Q = jnp.array(Q) + Q_local = convert_cart2harm(Q, 2) + axis_type = np.array( + [atom.axisType for atom in atomDicts.values()] + ) + axis_indices = np.vstack( + [atom.axis_indices for atom in atomDicts.values()] + ) + covalent_map = assemble_covalent(residueDicts, n_atoms) + + ## ind paras + pol = np.vstack( + [(atom.polarizabilityXX, atom.polarizabilityYY, atom.polarizabilityZZ) for atom in atomDicts.values()] + ) + pol = jnp.array(pol.astype(np.float32)) + pol = 1000*jnp.mean(pol,axis=1) + + tholes = np.vstack( + [atom.thole for atom in atomDicts.values()] + ) + tholes = jnp.array(tholes.astype(np.float32)) + tholes = jnp.mean(tholes,axis=1) + defaultTholeWidth=8 + + Uind_global = jnp.zeros([n_atoms,3]) + for i in range(n_atoms): + a = get_line_context(ref_dip,i+1) + b = a.split() + t = np.array([10*float(b[0]),10*float(b[1]),10*float(b[2])]) + Uind_global = Uind_global.at[i].set(t) + + + + lmax = 2 + pmax = 10 + + # construct the C list + c_list = np.zeros((3, n_atoms)) + a_list = np.zeros(n_atoms) + q_list = np.zeros(n_atoms) + b_list = np.zeros(n_atoms) + nmol=int(n_atoms/3) + for i in range(nmol): + a = i*3 + b = i*3+1 + c = i*3+2 + # dispersion coeff + c_list[0][a]=37.19677405 + c_list[0][b]=7.6111103 + c_list[0][c]=7.6111103 + c_list[1][a]=85.26810658 + c_list[1][b]=11.90220148 + c_list[1][c]=11.90220148 + c_list[2][a]=134.44874488 + c_list[2][b]=15.05074749 + c_list[2][c]=15.05074749 + # q + q_list[a] = -0.741706 + q_list[b] = 0.370853 + q_list[c] = 0.370853 + # b, Bohr^-1 + b_list[a] = 2.00095977 + b_list[b] = 1.999519942 + b_list[c] = 1.999519942 + # a, Hartree + a_list[a] = 458.3777 + a_list[b] = 0.0317 + a_list[c] = 0.0317 + + # Finish data preparation + # ------------------------------------------------------------------------------------- + # parameters should be ready: + # geometric variables: positions, box + # atomic parameters: Q_local, c_list + # topological parameters: covalent_map, mScales, pScales, dScales + # general force field setting parameters: rc, ethresh, lmax, pmax + + + # get neighbor list using jax_md + displacement_fn, shift_fn = space.periodic_general(box, fractional_coordinates=False) + neighbor_list_fn = partition.neighbor_list(displacement_fn, box, rc, 0, format=partition.OrderedSparse) + nbr = neighbor_list_fn.allocate(positions) + pairs = nbr.idx.T + + # electrostatic + pme_force = ADMPPmeForce(box, axis_type, axis_indices, covalent_map, rc, ethresh, lmax, lpol=True) + pme_force.update_env('kappa', 0.657065221219616) + E, F = pme_force.get_forces(positions, box, pairs, Q_local, pol, tholes, mScales, pScales, dScales) + print('# Electrostatic Energy (kJ/mol)') + # E = pme_force.get_energy(positions, box, pairs, Q_local, mScales, pScales, dScales) + E, F = pme_force.get_forces(positions, box, pairs, Q_local, pol, tholes, mScales, pScales, dScales, U_init=pme_force.U_ind) + print(E) + U_ind = pme_force.U_ind + # compare U_ind with reference + # for i in range(1024): + # for j in range(3): + # print(Uind_global[i*3, j], Uind_global[i*3, j], U_ind[i*3, j]) + diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 000000000..14a7a7497 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,9 @@ +site_name: DMFF +nav: + - Home: 'index.md' + - About: 'about.md' + - 'User Guide': + - 'tutorial': 'tutorial.md' + - 'Modules': + - 'ADMP': +theme: readthedocs diff --git a/setup.py b/setup.py index e69de29bb..e8ef2b134 100644 --- a/setup.py +++ b/setup.py @@ -0,0 +1,22 @@ +from setuptools import setup, find_packages + +install_requires = [ + "numpy", + "jax_md", + "openmm", +] +setup( + name='dmff', + version='0.0.1', + author='dptech.net', + # author_email='hermite@dptech.net', + description=('DMFF.'), + url='https://github.com/deepmodeling/DMFF', + license=None, + keywords='Differentiable', + install_requires=install_requires, + packages=find_packages(), + zip_safe=False, + #packages=packages, + entry_points={}, + include_package_data=True) \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/test_multipole.py b/tests/test_multipole.py new file mode 100644 index 000000000..7e1064ac7 --- /dev/null +++ b/tests/test_multipole.py @@ -0,0 +1,189 @@ +import jax.numpy as jnp +import numpy as np +import numpy.testing as npt +import pytest +from admp.multipole import convert_cart2harm +from admp.multipole import rot_global2local, rot_local2global + +class TestMultipoleGeometry: + @pytest.mark.parametrize( + "theta, expected", + [ + ( + jnp.array( + [ + [ + -1.0614, + 0.0, + 0.0, + -0.23671684, + 0.0452889, + 0.026121, + -0.0714102, + 0.0, + 0.0, + 0.0, + ], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [ + -1.0614, + 0.0, + 0.0, + -0.23671684, + 0.0452889, + 0.026121, + -0.0714102, + 0.0, + 0.0, + 0.0, + ], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + ] + ), + jnp.array( + [ + [ + -1.0614, + -0.23671684, + 0.0, + 0.0, + -0.0714102, + 0.0, + 0.0, + 0.01106659, + 0.0, + ], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [ + -1.0614, + -0.23671684, + 0.0, + 0.0, + -0.0714102, + 0.0, + 0.0, + 0.01106659, + 0.0, + ], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + ] + ), + ) + ], + ) + def test_convert_cart2harm(self, theta, expected): + + Q = convert_cart2harm(theta, 2) + npt.assert_allclose(Q, expected, rtol=1e-6) + + @pytest.mark.parametrize( + "Q_global, Q_local, local_frames", + [ + ( + jnp.array( + [ + [ + -1.0614, + -0.22052474, + -0.06001501, + 0.06165953, + -0.05764905, + -0.03114612, + 0.02651503, + 0.01010778, + 0.01109856, + ], + [0.5307, 0.0, -0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [ + -1.0614, + -0.04308845, + 0.22035345, + 0.07498398, + 0.02345808, + 0.01798866, + 0.01008533, + -0.05205911, + -0.03919373, + ], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + ] + ), + jnp.array( + [ + [ + -1.0614, + -0.23671684, + 0.0, + 0.0, + -0.0714102, + 0.0, + 0.0, + 0.01106659, + 0.0, + ], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [ + -1.0614, + -0.23671684, + 0.0, + 0.0, + -0.0714102, + 0.0, + 0.0, + 0.01106659, + 0.0, + ], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.5307, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], + ] + ), + jnp.array( + [ + [ + [-0.96165454, -0.17201543, 0.21361469], + [0.10460715, -0.95003253, -0.29410106], + [0.2535308, -0.26047802, 0.9315972], + ], + [ + [-0.38687626, -0.3113036, 0.8679958], + [-0.10460713, 0.9500325, 0.2941011], + [-0.91617906, 0.02298216, -0.4001096], + ], + [ + [0.7882788, -0.10109846, 0.606956], + [0.10460714, -0.95003265, -0.29410112], + [0.60636103, 0.29532564, -0.7383151], + ], + [ + [0.2869897, -0.94232714, -0.17221032], + [0.22607784, -0.10806667, 0.96809626], + [-0.93087363, -0.3167666, 0.18202528], + ], + [ + [-0.5616504, -0.8264594, 0.03890521], + [-0.22607785, 0.10806668, -0.9680963], + [0.79588807, -0.5525272, -0.24753988], + ], + [ + [-0.9122986, 0.32489008, 0.24931434], + [0.22607782, -0.10806666, 0.9680962], + [0.3414673, 0.9395574, 0.02513866], + ], + ] + ), + ) + ], + ) + def test_rot_global_local(self, Q_global, Q_local, local_frames): + + ans_Q = rot_local2global(Q_local, local_frames, 2) + npt.assert_allclose(ans_Q, Q_global, rtol=1e-6, atol=1e-6) + ans_Q = rot_global2local(Q_global, local_frames, 2) + npt.assert_allclose(ans_Q, Q_local, rtol=1e-6, atol=1e-6) diff --git a/tests/test_sptial.py b/tests/test_sptial.py new file mode 100644 index 000000000..e13c8d863 --- /dev/null +++ b/tests/test_sptial.py @@ -0,0 +1,143 @@ +import jax.numpy as jnp +import numpy as np +import numpy.testing as npt +import pytest +from admp.spatial import (build_quasi_internal, + generate_construct_local_frames, pbc_shift, + v_pbc_shift) + + +class TestSpatial: + @pytest.mark.parametrize( + "r1, r2, dr, norm_dr, expected", + [ + ( + jnp.array([[0, 0, 0]]), + jnp.array([[1, 0, 0]]), + jnp.array([[1, 0, 0]]), + jnp.array([1.0,]), + jnp.array([[[0.0, 1.0, 0.0], [0, 0, 1], [1, 0, 0]]]), + ), + ( + jnp.array([[0, 0, 0]]), + jnp.array([[1, 1, 0]]), + jnp.array([[1, 1, 0]]), + jnp.array([1.414213,]), + jnp.array( + [ + [ + [0.70710534, -0.70710814, 0.0], + [0.0, 0.0, -1.0000004], + [0.70710707, 0.70710707, 0.0], + ] + ] + ), + ), + ], + ) + def test_build_quasi_internal(self, r1, r2, dr, norm_dr, expected): + + local_frames = build_quasi_internal(r1, r2, dr, norm_dr) + npt.assert_allclose(local_frames, expected) + + @pytest.mark.parametrize( + "drvecs, box, box_inv, expected", + [ + ( + jnp.array([[0, 0, 0]]), + jnp.array([[4, 0, 0], [0, 4, 0], [0, 0, 4]]), + jnp.array([[0.25, 0, 0], [0, 0.25, 0], [0, 0, 0.25]]), + jnp.array([[0, 0, 0]]), + ), + ( + jnp.array([[3, 0, 0], [0, 3, 0], [0, 0, 3]]), + jnp.array([[4, 0, 0], [0, 4, 0], [0, 0, 4]]), + jnp.array([[0.25, 0, 0], [0, 0.25, 0], [0, 0, 0.25]]), + jnp.array([[-1, 0, 0], [0, -1, 0], [0, 0, -1]]), + ), + ( + jnp.array([[2, 0, 0], [0, 2, 0], [0, 0, 2]]), + jnp.array([[4, 0, 0], [0, 4, 0], [0, 0, 4]]), + jnp.array([[0.25, 0, 0], [0, 0.25, 0], [0, 0, 0.25]]), + jnp.array([[-2, 0, 0], [0, -2, 0], [0, 0, -2]]), + ), + ], + ) + def test_pbc_shift(self, drvecs, box, box_inv, expected): + npt.assert_allclose(pbc_shift(drvecs, box, box_inv), expected) + npt.assert_allclose(v_pbc_shift(drvecs, box, box_inv), expected) + + @pytest.mark.parametrize( + "axis_types, axis_indices, positions, box, expected_local_frames", + [ + ( + np.array([1, 0, 0, 1, 0, 0]), + np.array( + [ + [1, 2, -1], + [0, 2, -1], + [0, 1, -1], + [4, 5, -1], + [3, 5, -1], + [3, 4, -1], + ] + ), + jnp.array( + [ + [1.562, 24.46, 21.149], + [2.439, 24.438, 21.532], + [0.983, 24.178, 21.854], + [4.788, 24.609, 0.994], + [4.026, 25.138, 1.231], + [4.462, 23.712, 0.97], + ] + ), + jnp.array([[31.289, 0.0, 0.0], [0.0, 31.289, 0.0], [0.0, 0.0, 31.289]]), + np.array( + [ + [ + [-0.96165454, -0.17201543, 0.21361469], + [0.10460715, -0.95003253, -0.29410106], + [0.2535308, -0.26047802, 0.9315972], + ], + [ + [-0.38687626, -0.3113036, 0.8679958], + [-0.10460713, 0.9500325, 0.2941011], + [-0.91617906, 0.02298216, -0.4001096], + ], + [ + [0.7882788, -0.10109846, 0.606956], + [0.10460714, -0.95003265, -0.29410112], + [0.60636103, 0.29532564, -0.7383151], + ], + [ + [0.2869897, -0.94232714, -0.17221032], + [0.22607784, -0.10806667, 0.96809626], + [-0.93087363, -0.3167666, 0.18202528], + ], + [ + [-0.5616504, -0.8264594, 0.03890521], + [-0.22607785, 0.10806668, -0.9680963], + [0.79588807, -0.5525272, -0.24753988], + ], + [ + [-0.9122986, 0.32489008, 0.24931434], + [0.22607782, -0.10806666, 0.9680962], + [0.3414673, 0.9395574, 0.02513866], + ], + ] + ), + ) + ], + ) + def test_generate_construct_local_frames( + self, axis_types, axis_indices, positions, box, expected_local_frames + ): + construct_local_frame_fn = generate_construct_local_frames( + axis_types, axis_indices + ) + assert construct_local_frame_fn + npt.assert_allclose( + construct_local_frame_fn(positions, box), expected_local_frames, rtol=1e-6 + ) +