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_string5.py
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_string5.py
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#!/usr/bin/env python
"""
Hydronium ion + NH3 molecule. String method.
Moving the proton from H2O to NH3 keeping O and H atoms fixed.
Based on tutoparal/tstring_04.in
"""
import sys
import operator
import numpy as np
import abipy.abilab as abilab
import abipy.data as abidata
import abipy.flowtk as flowtk
from itertools import product
from abipy.benchmarks import bench_main, BenchmarkFlow
def make_input(): # pragma: no cover
"""
"""
pseudos = abidata.pseudos("8o_hard.paw", "7n.paw", "1h.paw")
# Crystal structure and definition of the path
xangst = np.fromstring("""
0.0000000000E+00 0.0000000000E+00 0.0000000000E+00
-3.7593832509E-01 -2.8581911534E-01 8.7109635973E-01
-3.8439081179E-01 8.6764073738E-01 -2.8530130333E-01
4.0000000000E+00 0.0000000000E+00 0.0000000000E+00
4.3461703447E+00 -9.9808458269E-02 -9.5466143436E-01
4.3190273240E+00 -7.8675247603E-01 5.6699786920E-01
4.3411410402E+00 8.7383785043E-01 4.0224838603E-01
1.0280313162E+00 2.2598784215E-02 1.5561763093E-02""", sep=" ").reshape((-1,3))
xangst_lastimg = np.fromstring("""
0.0000000000E+00 0.0000000000E+00 0.0000000000E+00
-3.0400286349E-01 -1.9039526061E-01 9.0873550186E-01
-3.2251946581E-01 9.0284480687E-01 -1.8824324581E-01
4.0000000000E+00 0.0000000000E+00 0.0000000000E+00
4.4876385468E+00 -1.4925704575E-01 -8.9716581956E-01
4.2142401901E+00 -7.8694929117E-01 6.3097154506E-01
4.3498225718E+00 8.7106686509E-01 4.2709343135E-01
2.9570301511E+00 5.5992672027E-02 -1.3560839453E-01""", sep=" ").reshape((-1,3))
structure = abilab.Structure.from_abivars(
acell=abilab.ArrayWithUnit([10, 5, 5], "ang").to("bohr"),
rprim=np.eye(3),
typat=[1, 3, 3, 2, 3, 3, 3, 3], # Type of atoms (H2O + NH3 + H)
znucl=[8.0, 7.0, 1.0],
xangst=xangst,
)
inp = abilab.AbinitInput(structure, pseudos)
inp.set_vars(
# Options for parallelism
paral_kgb=1,
# Input/output options
prtwf=0,
prtden=0,
prteig=0,
prtdensph=0,
timopt=-1,
# Convergence parameters
ecut=20.,
pawecutdg=40.,
# Control of the relaxation # TO BE SUPPRESSED WHEN USING IMGMOV keyword
#ionmov 3 # BFGS (Broyden) algorithm for ions relaxation
#optcell 0 # No cell optimization
#ntime 20 # Max. number of "time" steps
#tolmxf 5.0d-5 # Stopping criterion of relaxation cycle
# Control of the SCF cycle
toldff=5.0e-7,
nstep=50,
# Electronic configuration
nband=10,
occopt=1,
ixc="-001009", # Select LDA XC functional (LDA PZ from LibXC)
# BZ sampling
kptopt=0, # Scheme for k-points generation
nkpt=1,
kpt=3*[0.], # Explicit k-point (gamma point)
nsym=1, # No symmetry
natfix=2,
iatfix=[1, 4], # Keep O and N atoms fixed
charge=1.0, # Charge of the simulation cell
# IMAGE section.
nimage=12, # Number of points along the string
imgmov=2, # Selection of "String Method" algo
ntimimage=50, # Max. number of relaxation steps of the string
tolimg=0.0001, # Tol. criterion (will stop when average energy of cells < tolimg)
dynimage="0 10*1 0", # Keep first and last images fixed
fxcartfactor=1.0, # Time step for evolution step of string method.
prtvolimg=2, # Printing volume (0=full, 1=intermediate, 2=minimal)
xangst_lastimg=xangst_lastimg,
)
return inp
def build_flow(options): # pragma: no cover
flow = BenchmarkFlow(workdir=options.get_workdir(__file__), remove=options.remove)
template = make_input()
# Processor distribution.
#pconfs = [
# dict
#npimage=10, # CPU distribution over images
#npband=10,
#npfft=2,
#bandpp=1, # CPU distribution for 20 CPU cores per image
#]
# Get the list of possible parallel configurations from abinit autoparal.
#max_ncpus, min_eff = options.max_ncpus, options.min_eff
#print("Getting all autoparal configurations up to max_ncpus: ",max_ncpus," with efficiency >= ",min_eff)
#pconfs = template.abiget_autoparal_pconfs(max_ncpus, autoparal=1, verbose=options.verbose)
#if options.verbose:
#print(pconfs)
#%% nprocs_to_test = 200
#work = flowtk.Work()
#for d, omp_threads in product(pconfs, options.omp_list):
# mpi_procs = reduce(operator.mul, d.values(), 1)
# if not options.accept_mpi_omp(mpi_procs, omp_threads): continue
# manager = options.manager.new_with_fixed_mpi_omp(mpi_procs, omp_threads)
# print("wfoptalg:", wfoptalg, "done with MPI_PROCS:", mpi_procs, "and:", d)
# inp = template.new_with_vars(d)
# work.register_scf_task(inp, manager=manager)
#flow.register_work(work)
return flow.allocate()
@bench_main
def main(options): # pragma: no cover
if options.info:
# print doc string and exit.
print(__doc__)
return
return build_flow(options)
if __name__ == "__main__":
sys.exit(main())