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abinitio.py
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""" @package forcebalance.abinitio Ab-initio fitting module (energies, forces, resp).
@author Lee-Ping Wang
@date 05/2012
"""
import os
import shutil
from forcebalance.nifty import col, eqcgmx, flat, floatornan, fqcgmx, invert_svd, kb, printcool, bohrang, warn_press_key, warn_once, pvec1d, commadash, uncommadash, isint
import numpy as np
from forcebalance.target import Target
from forcebalance.molecule import Molecule, format_xyz_coord
from re import match, sub
import subprocess
from subprocess import PIPE
from forcebalance.finite_difference import fdwrap, f1d2p, f12d3p, in_fd
from collections import defaultdict, OrderedDict
import itertools
#from IPython import embed
#from _increment import AbInitio_Build
from forcebalance.output import getLogger
logger = getLogger(__name__)
class AbInitio(Target):
""" Subclass of Target for fitting force fields to ab initio data.
Currently Gromacs-X2, Gromacs, Tinker, OpenMM and AMBER are supported.
We introduce the following concepts:
- The number of snapshots
- The reference energies and forces (eqm, fqm) and the file they belong in (qdata.txt)
- The sampling simulation energies (emd0)
- The WHAM Boltzmann weights (these are computed externally and passed in)
- The QM Boltzmann weights (computed internally using the difference between eqm and emd0)
There are also these little details:
- Switches for whether to turn on certain Boltzmann weights (they stack)
- Temperature for the QM Boltzmann weights
- Whether to fit a subset of atoms
This subclass contains the 'get' method for building the objective
function from any simulation software (a driver to run the program and
read output is still required). The 'get' method can be overridden
by subclasses like AbInitio_GMX."""
def __init__(self,options,tgt_opts,forcefield):
"""
Initialization; define a few core concepts.
@todo Obtain the number of true atoms (or the particle -> atom mapping)
from the force field.
"""
## Initialize the base class
super(AbInitio,self).__init__(options,tgt_opts,forcefield)
#======================================#
# Options that are given by the parser #
#======================================#
## Number of snapshots
self.set_option(tgt_opts,'shots','ns')
## Whether to use WHAM Boltzmann weights
self.set_option(tgt_opts,'whamboltz','whamboltz')
## Whether to use the Sampling Correction
self.set_option(tgt_opts,'sampcorr','sampcorr')
## Whether to match Absolute Energies (make sure you know what you're doing)
self.set_option(tgt_opts,'absolute','absolute')
## Whether to use the Covariance Matrix
self.set_option(tgt_opts,'covariance','covariance')
## Whether to use QM Boltzmann weights
self.set_option(tgt_opts,'qmboltz','qmboltz')
## The temperature for QM Boltzmann weights
self.set_option(tgt_opts,'qmboltztemp','qmboltztemp')
## Number of atoms that we are fitting
self.set_option(tgt_opts,'fitatoms','fitatoms_in')
## Whether to fit Energies.
self.set_option(tgt_opts,'energy','energy')
## Whether to fit Forces.
self.set_option(tgt_opts,'force','force')
## Whether to fit Electrostatic Potential.
self.set_option(tgt_opts,'resp','resp')
self.set_option(tgt_opts,'resp_a','resp_a')
self.set_option(tgt_opts,'resp_b','resp_b')
## Weights for the three components.
self.set_option(tgt_opts,'w_energy','w_energy')
self.set_option(tgt_opts,'w_force','w_force')
self.set_option(tgt_opts,'force_map','force_map')
self.set_option(tgt_opts,'w_netforce','w_netforce')
self.set_option(tgt_opts,'w_torque','w_torque')
self.set_option(tgt_opts,'w_resp','w_resp')
## Option for how much data to write to disk.
self.set_option(tgt_opts,'writelevel','writelevel')
## Whether to do energy and force calculations for the whole trajectory, or to do
## one calculation per snapshot.
self.set_option(tgt_opts,'all_at_once','all_at_once')
## OpenMM-only option - whether to run the energies and forces internally.
self.set_option(tgt_opts,'run_internal','run_internal')
## Whether we have virtual sites (set at the global option level)
self.set_option(options,'have_vsite','have_vsite')
## Attenuate the weights as a function of energy
self.set_option(tgt_opts,'attenuate','attenuate')
## What is the energy denominator? (Valid for 'attenuate')
self.set_option(tgt_opts,'energy_denom','energy_denom')
## Set upper cutoff energy
self.set_option(tgt_opts,'energy_upper','energy_upper')
## Average forces over individual atoms ('atom') or all atoms ('all')
self.set_option(tgt_opts,'force_average')
## Assign a greater weight to MM snapshots that underestimate the QM energy (surfaces referenced to QM absolute minimum)
self.set_option(tgt_opts,'energy_asymmetry')
self.savg = (self.energy_asymmetry == 1.0 and not self.absolute)
self.asym = (self.energy_asymmetry != 1.0)
if self.asym:
if not self.all_at_once:
logger.error("Asymmetric weights only work when all_at_once is enabled")
raise RuntimeError
if self.qmboltz != 0.0:
logger.error("Asymmetric weights do not work with QM Boltzmann weights")
raise RuntimeError
#======================================#
# Variables which are set here #
#======================================#
## WHAM Boltzmann weights
self.boltz_wts = []
## QM Boltzmann weights
self.qmboltz_wts = []
## Reference (QM) energies
self.eqm = []
## Energies of the sampling simulation
self.emd0 = []
## Reference (QM) forces
self.fqm = []
## ESP grid points
self.espxyz = []
## ESP values
self.espval = []
## The qdata.txt file that contains the QM energies and forces
self.qfnm = os.path.join(self.tgtdir,"qdata.txt")
## Qualitative Indicator: average energy error (in kJ/mol)
self.e_err = 0.0
self.e_err_pct = None
## Qualitative Indicator: average force error (fractional)
self.f_err = 0.0
self.f_err_pct = 0.0
## Qualitative Indicator: "relative RMS" for electrostatic potential
self.esp_err = 0.0
self.nf_err = 0.0
self.nf_err_pct = 0.0
self.tq_err_pct = 0.0
## Whether to compute net forces and torques, or not.
self.use_nft = self.w_netforce > 0 or self.w_torque > 0
## Read in the trajectory file
self.mol = Molecule(os.path.join(self.root,self.tgtdir,self.coords),
top=(os.path.join(self.root,self.tgtdir,self.pdb) if hasattr(self, 'pdb') else None))
## Set the number of snapshots
if self.ns != -1:
self.mol = self.mol[:self.ns]
self.ns = len(self.mol)
## The number of (atoms + drude particles + virtual sites)
self.nparticles = len(self.mol.elem)
## Build keyword dictionaries to pass to engine.
engine_args = OrderedDict(self.OptionDict.items() + options.items())
del engine_args['name']
## Create engine object.
self.engine = self.engine_(target=self, mol=self.mol, **engine_args)
## Lists of atoms to do net force/torque fitting and excluding virtual sites.
self.AtomLists = self.engine.AtomLists
self.AtomMask = self.engine.AtomMask
## Read in the reference data
self.read_reference_data()
## The below two options are related to whether we want to rebuild virtual site positions.
## Rebuild the distance matrix if virtual site positions have changed
self.buildx = True
## Save the mvals from the last time we updated the vsites.
self.save_vmvals = {}
self.set_option(None, 'shots', val=self.ns)
def build_invdist(self, mvals):
for i in self.pgrad:
if 'VSITE' in self.FF.plist[i]:
if i in self.save_vmvals and mvals[i] != self.save_vmvals[i]:
self.buildx = True
break
if not self.buildx: return self.invdists
if any(['VSITE' in i for i in self.FF.map.keys()]) or self.have_vsite:
logger.info("\rGenerating virtual site positions.%s" % (" "*30))
pvals = self.FF.make(mvals)
self.mol.xyzs = self.engine.generate_positions()
# prepare the distance matrix for esp computations
if len(self.espxyz) > 0:
invdists = []
logger.info("\rPreparing the distance matrix... it will have %i * %i * %i = %i elements" % (self.ns, self.nesp, self.nparticles, self.ns * self.nesp * self.nparticles))
sn = 0
for espset, xyz in zip(self.espxyz, self.mol.xyzs):
logger.info("\rGenerating ESP distances for snapshot %i%s\r" % (sn, " "*50))
esparr = np.array(espset).reshape(-1,3)
# Create a matrix with Nesp rows and Natoms columns.
DistMat = np.array([[np.linalg.norm(i - j) for j in xyz] for i in esparr])
invdists.append(1. / (DistMat / bohrang))
sn += 1
for i in self.pgrad:
if 'VSITE' in self.FF.plist[i]:
self.save_vmvals[i] = mvals[i]
self.buildx = False
return np.array(invdists)
def compute_netforce_torque(self, xyz, force, QM=False):
# Convert an array of (3 * n_atoms) atomistic forces
# to an array of (3 * (n_forces + n_torques)) net forces and torques.
# This code is rather slow. It requires the system to have a list
# of masses and blocking numbers.
kwds = {"MoleculeNumber" : "molecule",
"ResidueNumber" : "residue",
"ChargeGroupNumber" : "chargegroup"}
if self.force_map == 'molecule' and 'MoleculeNumber' in self.AtomLists:
Block = self.AtomLists['MoleculeNumber']
elif self.force_map == 'residue' and 'ResidueNumber' in self.AtomLists:
Block = self.AtomLists['ResidueNumber']
elif self.force_map == 'chargegroup' and 'ChargeGroupNumber' in self.AtomLists:
Block = self.AtomLists['ChargeGroupNumber']
else:
logger.error('force_map keyword "%s" is invalid. Please choose from: %s\n' % (self.force_map, ', '.join(['"%s"' % kwds[k] for k in self.AtomLists.keys() if k in kwds])))
raise RuntimeError
nft = len(self.fitatoms)
# Number of particles that the force is acting on
nfp = force.reshape(-1,3).shape[0]
# Number of particles in the XYZ coordinates
nxp = xyz.shape[0]
# Number of particles in self.AtomLists
npr = len(self.AtomMask)
# Number of true atoms
nat = sum(self.AtomMask)
mask = np.array([i for i in range(npr) if self.AtomMask[i]])
if nfp not in [npr, nat]:
logger.error('Force contains %i particles but expected %i or %i\n' % (nfp, npr, nat))
raise RuntimeError
elif nfp == nat:
frc1 = force.reshape(-1,3)[:nft].flatten()
elif nfp == npr:
frc1 = force.reshape(-1,3)[mask][:nft].flatten()
if nxp not in [npr, nat]:
logger.error('Coordinates contains %i particles but expected %i or %i\n' % (nfp, npr, nat))
raise RuntimeError
elif nxp == nat:
xyz1 = xyz[:nft]
elif nxp == npr:
xyz1 = xyz[mask][:nft]
Block = list(np.array(Block)[mask])[:nft]
Mass = np.array(self.AtomLists['Mass'])[mask][:nft]
NetForces = []
Torques = []
for b in sorted(set(Block)):
AtomBlock = np.array([i for i in range(len(Block)) if Block[i] == b])
CrdBlock = np.array(list(itertools.chain(*[range(3*i, 3*i+3) for i in AtomBlock])))
com = np.sum(xyz1[AtomBlock]*np.outer(Mass[AtomBlock],np.ones(3)), axis=0) / np.sum(Mass[AtomBlock])
frc = frc1[CrdBlock].reshape(-1,3)
NetForce = np.sum(frc, axis=0)
xyzb = xyz1[AtomBlock]
Torque = np.zeros(3)
for a in range(len(xyzb)):
R = xyzb[a] - com
F = frc[a]
# I think the unit of torque is in nm x kJ / nm.
Torque += np.cross(R, F) / 10
NetForces += [i for i in NetForce]
# Increment the torques only if we have more than one atom in our block.
if len(xyzb) > 1:
Torques += [i for i in Torque]
netfrc_torque = np.array(NetForces + Torques)
self.nnf = len(NetForces)/3
self.ntq = len(Torques)/3
return netfrc_torque
def read_reference_data(self):
""" Read the reference ab initio data from a file such as qdata.txt.
@todo Add an option for picking any slice out of
qdata.txt, helpful for cross-validation
@todo Closer integration of reference data with program -
leave behind the qdata.txt format? (For now, I like the
readability of qdata.txt)
After reading in the information from qdata.txt, it is converted
into the GROMACS energy units (kind of an arbitrary choice);
forces (kind of a misnomer in qdata.txt) are multipled by -1
to convert gradients to forces.
We also subtract out the mean energies of all energy arrays
because energy/force matching does not account for zero-point
energy differences between MM and QM (i.e. energy of electrons
in core orbitals).
The configurations in force/energy matching typically come
from a the thermodynamic ensemble of the MM force field at
some temperature (by running MD, for example), and for many
reasons it is helpful to introduce non-Boltzmann weights in
front of these configurations. There are two options: WHAM
Boltzmann weights (for combining the weights of several
simulations together) and QM Boltzmann weights (for converting
MM weights into QM weights). Note that the two sets of weights
'stack'; i.e. they can be used at the same time.
A 'hybrid' ensemble is possible where we use 50% MM and 50% QM
weights. Please read more in LPW and Troy Van Voorhis, JCP
Vol. 133, Pg. 231101 (2010), doi:10.1063/1.3519043.
@todo The WHAM Boltzmann weights are generated by external
scripts (wanalyze.py and make-wham-data.sh) and passed in;
perhaps these scripts can be added to the ForceBalance
distribution or integrated more tightly.
Finally, note that using non-Boltzmann weights degrades the
statistical information content of the snapshots. This
problem will generally become worse if the ensemble to which
we're reweighting is dramatically different from the one we're
sampling from. We end up with a set of Boltzmann weights like
[1e-9, 1e-9, 1.0, 1e-9, 1e-9 ... ] and this is essentially just
one snapshot. I believe Troy is working on something to cure
this problem.
Here, we have a measure for the information content of our snapshots,
which comes easily from the definition of information entropy:
S = -1*Sum_i(P_i*log(P_i))
InfoContent = exp(-S)
With uniform weights, InfoContent is equal to the number of snapshots;
with horrible weights, InfoContent is closer to one.
"""
# Parse the qdata.txt file
for line in open(os.path.join(self.root,self.qfnm)):
sline = line.split()
if len(sline) == 0: continue
elif sline[0] == 'ENERGY':
self.eqm.append(float(sline[1]))
elif sline[0] == 'EMD0':
self.emd0.append(float(sline[1]))
elif sline[0] == 'FORCES':
self.fqm.append([float(i) for i in sline[1:]])
elif sline[0] == 'ESPXYZ':
self.espxyz.append([float(i) for i in sline[1:]])
elif sline[0] == 'ESPVAL':
self.espval.append([float(i) for i in sline[1:]])
# Ensure that all lists are of length self.ns
self.eqm = self.eqm[:self.ns]
self.emd0 = self.emd0[:self.ns]
self.fqm = self.fqm[:self.ns]
self.espxyz = self.espxyz[:self.ns]
self.espval = self.espval[:self.ns]
# Turn everything into arrays, convert to kJ/mol, and subtract the mean energy from the energy arrays
self.eqm = np.array(self.eqm)
self.eqm *= eqcgmx
if self.asym:
self.eqm -= np.min(self.eqm)
self.smin = np.argmin(self.eqm)
logger.info("Referencing all energies to the snapshot %i (minimum energy structure in QM)\n" % self.smin)
elif self.absolute:
logger.info("Fitting absolute energies. Make sure you know what you are doing!\n")
else:
self.eqm -= np.mean(self.eqm)
if len(self.fqm) > 0:
self.fqm = np.array(self.fqm)
self.fqm *= fqcgmx
self.qmatoms = range(self.fqm.shape[1]/3)
else:
logger.info("QM forces are not present, only fitting energies.\n")
self.force = 0
self.w_force = 0
# Here we may choose a subset of atoms to do the force matching.
if self.force:
# Build a list corresponding to the atom indices where we are fitting the forces.
if isint(self.fitatoms_in):
if int(self.fitatoms_in) == 0:
self.fitatoms = self.qmatoms
else:
warn_press_key("Provided an integer for fitatoms; will assume this means the first %i atoms" % int(self.fitatoms_in))
self.fitatoms = range(int(self.fitatoms_in))
else:
# If provided a "comma and dash" list, then expand the list.
self.fitatoms = uncommadash(self.fitatoms_in)
if len(self.fitatoms) > len(self.qmatoms):
warn_press_key("There are more fitting atoms than the total number of atoms in the QM calculation (something is probably wrong)")
else:
if self.w_force > 0:
if len(self.fitatoms) == len(self.qmatoms):
logger.info("Fitting the forces on all atoms\n")
else:
logger.info("Fitting the forces on atoms %s\n" % commadash(self.fitatoms))
logger.info("Pruning the quantum force matrix...\n")
selct = list(itertools.chain(*[[3*i+j for j in range(3)] for i in self.fitatoms]))
self.fqm = self.fqm[:, selct]
else:
self.fitatoms = []
self.nesp = len(self.espval[0]) if len(self.espval) > 0 else 0
if len(self.emd0) > 0:
self.emd0 = np.array(self.emd0)
self.emd0 -= np.mean(self.emd0)
if self.whamboltz == True:
if self.attenuate:
logger.error('whamboltz and attenuate are mutually exclusive\n')
raise RuntimeError
self.boltz_wts = np.array([float(i.strip()) for i in open(os.path.join(self.root,self.tgtdir,"wham-weights.txt")).readlines()])
# This is a constant pre-multiplier in front of every snapshot.
bar = printcool("Using WHAM MM Boltzmann weights.", color=4)
if os.path.exists(os.path.join(self.root,self.tgtdir,"wham-master.txt")):
whaminfo = open(os.path.join(self.root,self.tgtdir,"wham-master.txt")).readlines()
logger.info("From wham-master.txt, I can see that you're using %i generations\n" % len(whaminfo))
logger.info("Relative weight of each generation:\n")
shotcounter = 0
for line in whaminfo:
sline = line.split()
genshots = int(sline[2])
weight = np.sum(self.boltz_wts[shotcounter:shotcounter+genshots])/np.sum(self.boltz_wts)
logger.info(" %s, %i snapshots, weight %.3e\n" % (sline[0], genshots, weight))
shotcounter += genshots
else:
logger.info("Oops... WHAM files don't exist?\n")
logger.info(bar)
elif self.attenuate:
# Attenuate energies by an amount proportional to their
# value above the minimum.
eqm1 = self.eqm - np.min(self.eqm)
denom = self.energy_denom * 4.184 # kcal/mol to kJ/mol
upper = self.energy_upper * 4.184 # kcal/mol to kJ/mol
self.boltz_wts = np.ones(self.ns)
for i in range(self.ns):
if eqm1[i] > upper:
self.boltz_wts[i] = 0.0
elif eqm1[i] < denom:
self.boltz_wts[i] = 1.0 / denom
else:
self.boltz_wts[i] = 1.0 / np.sqrt(denom**2 + (eqm1[i]-denom)**2)
else:
self.boltz_wts = np.ones(self.ns)
if self.qmboltz > 0:
# LPW I haven't revised this section yet
# Do I need to?
logboltz = -(self.eqm - np.mean(self.eqm) - self.emd0 + np.mean(self.emd0)) / kb / self.qmboltztemp
logboltz -= np.max(logboltz) # Normalizes boltzmann weights
self.qmboltz_wts = np.exp(logboltz)
self.qmboltz_wts /= np.sum(self.qmboltz_wts)
# where simply gathers the nonzero values otherwise we get lots of NaNs
qbwhere = self.qmboltz_wts[np.where(self.qmboltz_wts)[0]]
# Compute ze InfoContent!
qmboltzent = -np.sum(qbwhere*np.log(qbwhere))
logger.info("Quantum Boltzmann weights are ON, the formula is exp(-b(E_qm-E_mm)),")
logger.info("distribution entropy is %.3f, equivalent to %.2f snapshots\n" % (qmboltzent, np.exp(qmboltzent)))
logger.info("%.1f%% is mixed into the MM boltzmann weights.\n" % (self.qmboltz*100))
else:
self.qmboltz_wts = np.ones(self.ns)
# At this point, self.fqm is a (number of snapshots) x (3 x number of atoms) array.
# Now we can transform it into a (number of snapshots) x (3 x number of residues + 3 x number of residues) array.
if self.use_nft:
self.nftqm = []
for i in range(len(self.fqm)):
self.nftqm.append(self.compute_netforce_torque(self.mol.xyzs[i], self.fqm[i]))
self.nftqm = np.array(self.nftqm)
self.fref = np.hstack((self.fqm, self.nftqm))
else:
self.fref = self.fqm
self.nnf = 0
self.ntq = 0
# Normalize Boltzmann weights.
self.boltz_wts /= sum(self.boltz_wts)
self.qmboltz_wts /= sum(self.qmboltz_wts)
def indicate(self):
Headings = ["Observable", "Difference\n(Calc-Ref)", "Denominator\n RMS (Ref)", " Percent \nDifference", "Weight", "Contribution"]
Data = OrderedDict([])
if self.energy:
Data['Energy (kJ/mol)'] = ["%8.4f" % self.e_err,
"%8.4f" % self.e_ref,
"%.4f%%" % (self.e_err_pct*100),
"%.3f" % self.w_energy,
"%8.4f" % self.e_ctr]
if self.force:
Data['Gradient (kJ/mol/A)'] = ["%8.4f" % (self.f_err/10),
"%8.4f" % (self.f_ref/10),
"%.4f%%" % (self.f_err_pct*100),
"%.3f" % self.w_force,
"%8.4f" % self.f_ctr]
if self.use_nft:
Data['Net Force (kJ/mol/A)'] = ["%8.4f" % (self.nf_err/10),
"%8.4f" % (self.nf_ref/10),
"%.4f%%" % (self.nf_err_pct*100),
"%.3f" % self.w_netforce,
"%8.4f" % self.nf_ctr]
Data['Torque (kJ/mol/rad)'] = ["%8.4f" % self.tq_err,
"%8.4f" % self.tq_ref,
"%.4f%%" % (self.tq_err_pct*100),
"%.3f" % self.w_torque,
"%8.4f" % self.tq_ctr]
if self.resp:
Data['Potential (a.u.'] = ["%8.4f" % (self.esp_err/10),
"%8.4f" % (self.esp_ref/10),
"%.4f%%" % (self.esp_err_pct*100),
"%.3f" % self.w_resp,
"%8.4f" % self.esp_ctr]
self.printcool_table(data=Data, headings=Headings, color=0)
if self.force:
logger.info("Maximum force error on atom %i (%s), frame %i, %8.4f kJ/mol/A\n" % (self.maxfatom, self.mol.elem[self.fitatoms[self.maxfatom]], self.maxfshot, self.maxdf/10))
def energy_all(self):
if hasattr(self, 'engine'):
return self.engine.energy().reshape(-1,1)
else:
logger.error("Target must contain an engine object\n")
raise NotImplementedError
def energy_force_all(self):
if hasattr(self, 'engine'):
return self.engine.energy_force()
else:
logger.error("Target must contain an engine object\n")
raise NotImplementedError
def energy_force_transform(self):
if self.force:
M = self.energy_force_all()
selct = [0] + list(itertools.chain(*[[1+3*i+j for j in range(3)] for i in self.fitatoms]))
M = M[:, selct]
if self.use_nft:
Nfts = []
for i in range(len(M)):
Fm = M[i][1:]
Nft = self.compute_netforce_torque(self.mol.xyzs[i], Fm)
Nfts.append(Nft)
Nfts = np.array(Nfts)
return np.hstack((M, Nfts))
else:
return M
else:
return self.energy_all()
def energy_one(self, i):
if hasattr(self, 'engine'):
return self.engine.energy_one(i)
else:
logger.error("Target must contain an engine object\n")
raise NotImplementedError
def energy_force_one(self, i):
if hasattr(self, 'engine'):
return self.engine.energy_force_one(i)
else:
logger.error("Target must contain an engine object\n")
raise NotImplementedError
def energy_force_transform_one(self,i):
if self.force:
M = self.energy_force_one(i)
selct = [0] + list(itertools.chain(*[[1+3*i+j for j in range(3)] for i in self.fitatoms]))
M = M[:, selct]
if self.use_nft:
Fm = M[1:]
Nft = self.compute_netforce_torque(self.mol.xyzs[i], Fm)
return np.hstack((M, Nft))
else:
return M
else:
return self.energy_one()
def get_energy_force(self, mvals, AGrad=False, AHess=False):
"""
LPW 06-30-2013
This subroutine builds the objective function (and optionally
its derivatives) from a general simulation software. This is
in contrast to using GROMACS-X2, which computes the objective
function and prints it out; then 'get' only needs to call
GROMACS and read it in.
This subroutine interfaces with simulation software 'drivers'.
The driver is only expected to give the energy and forces.
Now this subroutine may sound trivial since the objective
function is simply a least-squares quantity (M-Q)^2 - but
there are a number of nontrivial considerations. I will list
them here.
0) Polytensor formulation: Because there may exist covariance
between different components of the force (or covariance
between the energy and the force), we build the objective
function by taking outer products of vectors that have the
form [E F_1x F_1y F_1z F_2x F_2y ... ], and then we trace it
with the inverse of the covariance matrix to get the objective
function.
This version implements both the polytensor formulation and the standard formulation.
1) Boltzmann weights and normalization: Each snapshot has its
own Boltzmann weight, which may or may not be normalized.
This subroutine does the normalization automatically.
2) Subtracting out the mean energy gap: The zero-point energy
difference between reference data and simulation is
meaningless. This subroutine subtracts it out.
3) Hybrid ensembles: This program builds a combined objective
function from both MM and QM ensembles, which is rigorously
better than using a single ensemble.
Note that this subroutine does not do EVERYTHING that
GROMACS-X2 can do, which includes:
1) Internal coordinate systems
2) 'Sampling correction' (deprecated, since it doesn't seem to work)
3) Analytic derivatives
In the previous code (ForTune) this subroutine used analytic
first derivatives of the energy and force to build the
derivatives of the objective function. Here I will take a
simplified approach, because building the derivatives are
cumbersome. For now we will return the objective function
ONLY. A two-point central difference should give us the first
and diagonal second derivative anyhow.
@todo Parallelization over snapshots is not implemented yet
@param[in] mvals Mathematical parameter values
@param[in] AGrad Switch to turn on analytic gradient
@param[in] AHess Switch to turn on analytic Hessian
@return Answer Contribution to the objective function
"""
cv = self.covariance # Whether covariance matrix is turned on
if cv and (AGrad or AHess):
warn_press_key("Covariance is turned on, gradients requested but not implemented (will skip over grad.)")
Answer = {}
# Create the new force field!!
pvals = self.FF.make(mvals)
#======================================#
# Copied from the old ForTune code #
#======================================#
nat = len(self.fitatoms)
nnf = self.nnf
ntq = self.ntq
NC = 3*nat
NCP1 = 3*nat+1
if self.use_nft:
NCP1 += 3*(nnf + ntq)
NP = self.FF.np
NS = self.ns
#==============================================================#
# Note: Because of hybrid ensembles, we form two separate #
# objective functions. This means the code is (trivially) #
# doubled in length because there are two sets of Boltzmann #
# weights. In principle I could write a general subroutine #
# for a single set of Boltzmann weights and call it twice, but #
# that would require me to loop over the snapshots twice. #
# However, after the loop over snapshots, the subroutine that #
# builds the objective function (build_objective, above) is #
# called twice using the MM-ensemble and QM-ensemble #
# variables. #
# #
# STEP 1: Form all of the arrays. #
# #
# An explanation of variable names follows. #
# Z(Y) = The partition function in the MM (QM) ensemble #
# M0_M = The mean of the MM values in the MM ensemble #
# QQ_Q = The unnormalized expected value of <Q(X)Q> #
# Later on we divide these quantities by Z to normalize, #
# and optionally Q0(X)Q0 is subtracted from QQ to get the #
# covariance. #
#==============================================================#
if (self.w_energy == 0.0 and self.w_force == 0.0 and self.w_netforce == 0.0 and self.w_torque == 0.0):
AGrad = False
AHess = False
Z = 0.0
Y = 0.0
# The average force / net force per atom in kJ/mol/nm. (Added LPW 6/30)
# Torques are in kj/mol/nm x nm.
qF_M = 0
qN_M = 0
qT_M = 0
qF_Q = 0
qN_Q = 0
qT_Q = 0
dF_M = 0
dN_M = 0
dT_M = 0
dF_Q = 0
dN_Q = 0
dT_Q = 0
Q = np.zeros(NCP1)
# Mean quantities
M0_M = np.zeros(NCP1)
Q0_M = np.zeros(NCP1)
M0_Q = np.zeros(NCP1)
Q0_Q = np.zeros(NCP1)
if cv:
QQ_M = np.zeros((NCP1,NCP1))
QQ_Q = np.zeros((NCP1,NCP1))
#==============================================================#
# Objective function polytensors: This is formed in a loop #
# over snapshots by taking the outer product (Q-M)(X)(Q-M), #
# multiplying by the Boltzmann weight, and then summing. #
#==============================================================#
SPiXi = np.zeros((NCP1,NCP1))
SRiXi = np.zeros((NCP1,NCP1))
else:
# Derivatives
M_p = np.zeros((NP,NCP1))
M_pp = np.zeros((NP,NCP1))
X0_M = np.zeros(NCP1)
QQ_M = np.zeros(NCP1)
X0_Q = np.zeros(NCP1)
Q0_Q = np.zeros(NCP1)
QQ_Q = np.zeros(NCP1)
# Means of gradients
M0_M_p = np.zeros((NP,NCP1))
M0_Q_p = np.zeros((NP,NCP1))
M0_M_pp = np.zeros((NP,NCP1))
M0_Q_pp = np.zeros((NP,NCP1))
# Objective functions
SPiXi = np.zeros(NCP1)
SRiXi = np.zeros(NCP1)
# Debug: Store all objective function contributions
XiAll = np.zeros((NS, NCP1))
if AGrad:
SPiXi_p = np.zeros((NP,NCP1))
SRiXi_p = np.zeros((NP,NCP1))
X2_M_p = np.zeros(NP)
X2_Q_p = np.zeros(NP)
if AHess:
SPiXi_pq = np.zeros((NP,NP,NCP1))
SRiXi_pq = np.zeros((NP,NP,NCP1))
X2_M_pq = np.zeros((NP,NP))
X2_Q_pq = np.zeros((NP,NP))
M_all = np.zeros((NS,NCP1))
if AGrad and self.all_at_once:
dM_all = np.zeros((NS,NP,NCP1))
ddM_all = np.zeros((NS,NP,NCP1))
QBN = np.dot(self.qmboltz_wts[:NS],self.boltz_wts[:NS])
#==============================================================#
# STEP 2: Loop through the snapshots. #
#==============================================================#
if self.all_at_once:
logger.debug("\rExecuting\r")
M_all = self.energy_force_transform()
if self.asym:
M_all[:, 0] -= M_all[self.smin, 0]
if not cv and (AGrad or AHess):
def callM(mvals_):
logger.debug("\r")
pvals = self.FF.make(mvals_)
return self.energy_force_transform()
for p in self.pgrad:
dM_all[:,p,:], ddM_all[:,p,:] = f12d3p(fdwrap(callM, mvals, p), h = self.h, f0 = M_all)
if self.asym:
dM_all[:, p, 0] -= dM_all[self.smin, p, 0]
ddM_all[:, p, 0] -= ddM_all[self.smin, p, 0]
if self.force and not in_fd():
self.maxfatom = -1
self.maxfshot = -1
self.maxdf = 0.0
for i in range(NS):
if i % 100 == 0:
logger.debug("\rIncrementing quantities for snapshot %i\r" % i)
# Build Boltzmann weights and increment partition function.
P = self.boltz_wts[i]
Z += P
R = self.qmboltz_wts[i]*self.boltz_wts[i] / QBN
Y += R
# Recall reference (QM) data
Q[0] = self.eqm[i]
if self.force:
Q[1:] = self.fref[i,:].copy()
# Increment the average quantities.
if cv:
QQ = np.outer(Q,Q)
else:
QQ = Q*Q
# Call the simulation software to get the MM quantities.
if self.all_at_once:
M = M_all[i]
else:
if i % 100 == 0:
logger.debug("Shot %i\r" % i)
M = self.energy_force_transform_one(i)
M_all[i,:] = M.copy()
if not cv:
X = M-Q
boost = 1.0
if self.asym and X[0] < 0.0:
boost = self.energy_asymmetry
# Increment the average values.
a = 1
if self.force:
dfrcarray_ = np.array([np.linalg.norm(M[a+3*j:a+3*j+3] - Q[a+3*j:a+3*j+3]) for j in range(nat)])
if not in_fd() and np.max(dfrcarray_) > self.maxdf:
self.maxdf = np.max(dfrcarray_)
self.maxfatom = np.argmax(dfrcarray_)
self.maxfshot = i
dfrcarray = np.mean(dfrcarray_)
qfrcarray = np.mean(np.array([np.linalg.norm(Q[a+3*j:a+3*j+3]) for j in range(nat)]))
dF_M += P*dfrcarray
dF_Q += R*dfrcarray
qF_M += P*qfrcarray
qF_Q += R*qfrcarray
a += 3*nat
if self.use_nft:
dnfrcarray = np.mean(np.array([np.linalg.norm(M[a+3*j:a+3*j+3] - Q[a+3*j:a+3*j+3]) for j in range(nnf)]))
qnfrcarray = np.mean(np.array([np.linalg.norm(Q[a+3*j:a+3*j+3]) for j in range(nnf)]))
dN_M += P*dnfrcarray
dN_Q += R*dnfrcarray
qN_M += P*qnfrcarray
qN_Q += R*qnfrcarray
a += 3*nnf
dtrqarray = np.mean(np.array([np.linalg.norm(M[a+3*j:a+3*j+3] - Q[a+3*j:a+3*j+3]) for j in range(ntq)]))
qtrqarray = np.mean(np.array([np.linalg.norm(Q[a+3*j:a+3*j+3]) for j in range(ntq)]))
dT_M += P*dtrqarray
dT_Q += R*dtrqarray
qT_M += P*qtrqarray
qT_Q += R*qtrqarray
# The [0] indicates that we are fitting the RMS force and not the RMSD
# (without the covariance, subtracting a mean force doesn't make sense.)
M0_M[0] += P*M[0]
M0_Q[0] += R*M[0]
Q0_M[0] += P*Q[0]
Q0_Q[0] += R*Q[0]
QQ_M += P*QQ
QQ_Q += R*QQ
if not cv:
X0_M[0] += P*X[0]
X0_Q[0] += R*X[0]
# Increment the objective function.
if cv:
Xi = np.outer(M,M) - 2*np.outer(Q,M) + np.outer(Q,Q)
else:
Xi = X**2
Xi[0] *= boost
XiAll[i] = Xi.copy()
SPiXi += P * Xi
SRiXi += R * Xi
#==============================================================#
# STEP 2a: Increment gradients and mean quantities. #
# This is only implemented for the case without covariance. #
#==============================================================#
if not cv:
for p in self.pgrad:
if not AGrad: continue
if self.all_at_once:
M_p[p] = dM_all[i, p]
M_pp[p] = ddM_all[i, p]
else:
def callM(mvals_):
if i % 100 == 0:
logger.debug("\r")
pvals = self.FF.make(mvals_)
return self.energy_force_transform_one(i)
M_p[p],M_pp[p] = f12d3p(fdwrap(callM, mvals, p), h = self.h, f0 = M)
# The [0] indicates that we are fitting the RMS force and not the RMSD
# (without the covariance, subtracting a mean force doesn't make sense.)
if all(M_p[p] == 0): continue
M0_M_p[p][0] += P * M_p[p][0]
M0_Q_p[p][0] += R * M_p[p][0]
#M0_M_pp[p][0] += P * M_pp[p][0]
#M0_Q_pp[p][0] += R * M_pp[p][0]
Xi_p = 2 * X * M_p[p]
Xi_p[0] *= boost
SPiXi_p[p] += P * Xi_p
SRiXi_p[p] += R * Xi_p
if not AHess: continue
if self.all_at_once:
M_pp[p] = ddM_all[i, p]
# This formula is more correct, but perhapsively convergence is slower.
#Xi_pq = 2 * (M_p[p] * M_p[p] + X * M_pp[p])
# Gauss-Newton formula for approximate Hessian
Xi_pq = 2 * (M_p[p] * M_p[p])
Xi_pq[0] *= boost
SPiXi_pq[p,p] += P * Xi_pq
SRiXi_pq[p,p] += R * Xi_pq
for q in range(p):
if all(M_p[q] == 0): continue
if q not in self.pgrad: continue
Xi_pq = 2 * M_p[p] * M_p[q]
Xi_pq[0] *= boost
SPiXi_pq[p,q] += P * Xi_pq
SRiXi_pq[p,q] += R * Xi_pq
# Dump energies and forces to disk.
M_all_print = M_all.copy()
if self.savg:
M_all_print[:,0] -= np.average(M_all_print[:,0], weights=self.boltz_wts)
if self.force:
Q_all_print = np.hstack((col(self.eqm),self.fref))
else:
Q_all_print = col(self.eqm)
if self.savg:
QEtmp = np.array(Q_all_print[:,0]).flatten()
Q_all_print[:,0] -= np.average(QEtmp, weights=self.boltz_wts)
if self.attenuate:
QEtmp = np.array(Q_all_print[:,0]).flatten()
Q_all_print[:,0] -= np.min(QEtmp)
MEtmp = np.array(M_all_print[:,0]).flatten()
M_all_print[:,0] -= np.min(MEtmp)
if self.writelevel > 1:
np.savetxt('M.txt',M_all_print)
np.savetxt('Q.txt',Q_all_print)
if self.writelevel > 0:
EnergyComparison = np.hstack((col(Q_all_print[:,0]),col(M_all_print[:,0])))
np.savetxt('QM-vs-MM-energies.txt',EnergyComparison)
WeightComparison = np.hstack((col(Q_all_print[:,0]),col(self.boltz_wts)))
np.savetxt('QM-vs-Wts.txt',WeightComparison)
if self.force and self.writelevel > 1:
# Write .xyz files which can be viewed in vmd.
QMTraj = self.mol[:].atom_select(self.fitatoms)
Mforce_obj = QMTraj[:]
Qforce_obj = QMTraj[:]
Mforce_print = np.array(M_all_print[:,1:3*nat+1])
Qforce_print = np.array(Q_all_print[:,1:3*nat+1])
Dforce_norm = np.array([np.linalg.norm(Mforce_print[i,:] - Qforce_print[i,:]) for i in range(NS)])
MaxComp = np.max(np.abs(np.vstack((Mforce_print,Qforce_print)).flatten()))
Mforce_print /= MaxComp
Qforce_print /= MaxComp
for i in range(NS):
Mforce_obj.xyzs[i] = Mforce_print[i, :].reshape(-1,3)
Qforce_obj.xyzs[i] = Qforce_print[i, :].reshape(-1,3)
# if nat < len(self.qmatoms):
# Fpad = np.zeros((len(self.qmatoms) - nat, 3))
# Mforce_obj.xyzs[i] = np.vstack((Mforce_obj.xyzs[i], Fpad))
# Qforce_obj.xyzs[i] = np.vstack((Qforce_obj.xyzs[i], Fpad))
if Mforce_obj.na != Mforce_obj.xyzs[0].shape[0]:
print Mforce_obj.na
print Mforce_obj.xyzs[0].shape[0]
warn_once('\x1b[91mThe printing of forces is not set up correctly. Not printing forces. Please report this issue.\x1b[0m')
else:
if self.writelevel > 1:
QMTraj.write('coords.xyz')
Mforce_obj.elem = ['H' for i in range(Mforce_obj.na)]
Mforce_obj.write('MMforce.xyz')
Qforce_obj.elem = ['H' for i in range(Qforce_obj.na)]
Qforce_obj.write('QMforce.xyz')
np.savetxt('Dforce_norm.dat', Dforce_norm)
#==============================================================#
# STEP 3: Build the (co)variance matrix and invert it. #
# In the case of no covariance, this is just a diagonal matrix #
# with the RMSD energy in [0,0] and the RMS gradient in [n, n] #
#==============================================================#
logger.debug("Done with snapshots, building objective function now\r")
if (self.w_energy > 0.0 or self.w_force > 0.0 or self.w_netforce > 0.0 or self.w_torque > 0.0):
wtot = self.w_energy + self.w_force + self.w_netforce + self.w_torque
EWt = self.w_energy / wtot
FWt = self.w_force / wtot
NWt = self.w_netforce / wtot
TWt = self.w_torque / wtot
else:
EWt = 0.0
FWt = 0.0
NWt = 0.0
TWt = 0.0
# Build the weight vector/matrix, so the force contribution is suppressed by 1/3N
if cv:
WM = np.zeros((NCP1,NCP1))
WM[0,0] = np.sqrt(EWt)
start = 1
block = 3*nat
end = start + block
for i in range(start, end):
WM[i, i] = np.sqrt(FWt / block)
if self.use_nft:
start = end
block = 3*nnf
end = start + block
for i in range(start, end):
WM[i, i] = np.sqrt(NWt / block)
start = end
block = 3*ntq
end = start + block
for i in range(start, end):
WM[i, i] = np.sqrt(TWt / block)
else:
WM = np.zeros(NCP1)
WM[0] = np.sqrt(EWt)
if self.force: