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qcengineGAMESScalc.py
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qcengineGAMESScalc.py
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import numpy as np
from ase.calculators.calculator import Calculator
from ase.units import Ha, Ang, Bohr
# For QCEngine:
import qcengine as qcng
import qcelemental as qcel
class qcengineGAMESScalculator(Calculator):
implemented_properties = ['energy', 'forces']
def __init__(
self,
input_path,
n_threads=1,
E_to_eV=Ha,
F_to_eV_Ang=Ha/Bohr,
use_torch=False,
*args,
**kwargs
):
"""
ASE calculator for the QCEngine/GAMESS ab initio module
A calculator takes atomic numbers and atomic positions from an Atoms object and calculates the energy and forces.
Note
----
ASE uses eV and Angstrom as energy and length unit, respectively. Unless the paramerters `E_to_eV` and `F_to_eV_Ang` are specified, the sGDML model is assumed to use kcal/mol and Angstorm and the appropriate conversion factors are set accordingly.
Here is how to find them: `ASE units <https://wiki.fysik.dtu.dk/ase/ase/units.html>`_.
Parameters
----------
input_path : :obj:`str`
Path to a QCEngine/GAMESS input file
E_to_eV : float, optional
Conversion factor from whatever energy unit is used by the model to eV. By default this parameter is set to convert from kcal/mol.
F_to_eV_Ang : float, optional
Conversion factor from whatever length unit is used by the model to Angstrom. By default, the length unit is not converted (assumed to be in Angstrom)
use_torch : boolean, optional
Use PyTorch to calculate predictions
"""
super(qcengineGAMESScalculator, self).__init__(*args, **kwargs)
# Default values for the input
self.maxiter = 10000
self.d_convergence = 1.0e-7
self.e_convergence = 1.0e-8
self.referencemethod = 'uhf'
self.freeze_core = 0
self.df_ints_io = "None"
self.method = 'b3lyp' # LevelOfTheory
self.basis_set = '6-31g*' # BasisSet
self.mulliken = 0
self.charge = 0
self.multiplicity = 2 # 2s+1
self.memory = 1500 # In MB
self.scratchdir = "/tmp"
# Read in the input
with open(input_path, 'r') as f:
for line in f:
strippedline=" ".join(line.split())
entries = strippedline.split(" ")
if (entries[0] == "memory"): self.memory = int(entries[1])
if (entries[0] == "scratchdir"): self.scratchdir = str(entries[1])
if (entries[0] == "referencemethod"): self.referencemethod = str(entries[1])
if (entries[0] == "freeze_core"): self.freeze_core = int(entries[1])
if (entries[0] == "df_ints_io"): self.df_ints_io = str(entries[1])
if (entries[0] == "method"): self.method = str(entries[1])
if (entries[0] == "basis_set"): self.basis_set = str(entries[1])
if (entries[0] == "mulliken"): self.mulliken = int(entries[1])
if (entries[0] == "charge"): self.charge = int(entries[1])
if (entries[0] == "multiplicity"): self.multiplicity = int(entries[1])
# By default, we start off with no initial MO guess
self.ref_wfn = None
self.Norb = None
# Prepare a minimally descriptive "model" for QCEngine/GAMESS
# GAMESS basis set documentation: https://myweb.liu.edu/~nmatsuna/gamess/input/BASIS.html
# QCEngine/GAMESS methods documentation: https://github.com/MolSSI/QCEngine/blob/f5f6da3751373fa9b57ea484cbf71416ba679743/qcengine/programs/gamess/germinate.py
self.model = {"method":self.method, "basis":self.basis_set}
# Prepare the environment and keywords for QCEngine/GAMESS
self.keywords = {
"contrl__scftyp" : self.referencemethod,
"contrl__maxit" : 200, # For some reason, 200 iterations is the maximum
"contrl__coord" : "unique", # Turns out this does nothing in QCEngine
"scf__diis" : ".true.",
"scf__ethrsh" : 1.0, # DIIS error to start DIIS (deafult = 0.5 hartree)
"scf__conv" : self.d_convergence,
}
self.MPIconfig = {
# "use_mpiexec" : True,
# "mpiexec_command": mpiexec_command,
"scratch_directory":".",
"nnodes": 1,
"ncores": n_threads,
# "cores_per_rank": 2,
}
# Set the number of threads used in this session
self.n_tasks = n_threads
# Converts energy from the unit used by the ab initio method to eV.
self.E_to_eV = E_to_eV
# Converts length from the unit used in the ab initio method to Ang.
self.Ang_to_R = F_to_eV_Ang / E_to_eV
# Converts force from the unit used by the abinitio method to eV/Ang.
self.F_to_eV_Ang = F_to_eV_Ang
print("Summary of QCEnginge ab initio input file (for GAMESS)")
print(" Model:", self.model)
print(" Keywords:", self.keywords)
print(" nproc:", self.n_tasks)
def calculate(self, atoms=None, *args, **kwargs):
super(qcengineGAMESScalculator, self).calculate(atoms, *args, **kwargs)
# Convert model units to ASE default units
# r = np.array(atoms.get_positions()) * self.Ang_to_R
out = ""
for anAtom in atoms:
out += anAtom.symbol + " " + str(anAtom.position[0]) + " " + str(anAtom.position[1]) + " " + str(anAtom.position[2]) + "\n"
# Fixing CoM and orientation and turning off symmetry to prevent molecular translation
mol = qcel.models.Molecule.from_data(out, fix_com=True, fix_orientation=True, fix_symmetry="C1") # Doesn't seem to work?
# mol = qcel.models.Molecule.from_data(out, fix_com=True, fix_symmetry="C1")
# Try the SCF convergence a few times with different approaches:
# (1) Try using the reference wavefunction (from the previous step)
# (2) Try the default superposition of atomic densities (SAD)
# tmp_d_convergence = self.d_convergence
# tmp_e_convergence = self.e_convergence
# for i in range(100):
# if (self.ref_wfn is None):
# try:
# e, self.ref_wfn = psi4.energy(self.psi4method,write_orbitals=self.movecs,return_wfn=True)
# psi4.set_options({'d_convergence': self.d_convergence})
# psi4.set_options({'e_convergence': self.e_convergence})
# break
# except SCFConvergenceError:
# if (i==10):
# raise SCFConvergenceError("SCF convergence failed too many times in a row!")
# print("SCF convergence ({0}) ... will now increase convergence threshold by a factor of 3".format(i))
# tmp_d_convergence = tmp_d_convergence * 3
# tmp_e_convergence = tmp_e_convergence * 3
# psi4.set_options({'d_convergence': tmp_d_convergence})
# psi4.set_options({'e_convergence': tmp_e_convergence})
# else:
# try:
# e, self.ref_wfn = psi4.energy(self.psi4method,restart_file=self.movecs,write_orbitals=self.movecs,return_wfn=True)
# break
# except SCFConvergenceError:
# print("SCF convergence (0) ... will now use a SAD guess")
# self.ref_wfn = None
inp = qcel.models.AtomicInput(molecule=mol, driver="gradient", model=self.model, keywords=self.keywords)
if (self.ref_wfn is not None):
inp.extras["VECguess"] = self.ref_wfn
inp.extras["VECnorb"] = self.Norb
f_results = qcng.compute(inp, 'gamess', task_config=self.MPIconfig)
self.ref_wfn = f_results.extras["VEC"]
self.Norb = f_results.extras["VECnorb"]
e = float(f_results.extras["qcvars"]["CURRENT ENERGY"])
f = f_results.return_result
# f = -np.array(f)
f = np.array(f)
# Make sure that the gradient was correctly read in
assert len(atoms) == len(f)
# If there are 'extra' things, do them here (e.g., Mulliken analysis)
# if (self.mulliken > 0):
# self.oeprop = OEProp(self.ref_wfn)
# self.oeprop.add("MULLIKEN_CHARGES")
# self.oeprop.compute()
# Convert model units to ASE default units (eV and Ang)
e *= self.E_to_eV
f *= -self.F_to_eV_Ang
f = f.reshape(-1, 3)
Natoms = len(atoms)
print("")
print("QCEngine flag 1")
print(Natoms)
print("Energy: ", e)
#for anAtom in atoms:
for i in range(Natoms):
anAtom = atoms[i]
print("{0:2s} {1:16.8f} {2:16.8f} {3:16.8f} {4:18.10f} {5:18.10f} {6:18.10f}".format(anAtom.symbol,*anAtom.position, *f[i]))
print("")
self.results = {'energy': e, 'forces': f}
def get_forces(self, atoms=None, force_consistent=False):
forces = self.get_property('forces', atoms)
return forces