#!/usr/bin/env python
# Copyright 2019 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#
'''
Mole class for MM particles
'''
import numpy
from pyscf import gto
from pyscf.gto.mole import is_au
from pyscf.data.elements import charge
from pyscf.lib import param
[docs]
class Mole(gto.Mole):
'''Mole class for MM particles.
Args:
atoms : geometry of MM particles (unit Bohr).
| [[atom1, (x, y, z)],
| [atom2, (x, y, z)],
| ...
| [atomN, (x, y, z)]]
Kwargs:
charges : 1D array
fractional charges of MM particles
zeta : 1D array
Gaussian charge distribution parameter.
rho(r) = charge * Norm * exp(-zeta * r^2)
'''
def __init__(self, atoms, charges=None, zeta=None):
gto.Mole.__init__(self)
self.atom = self._atom = atoms
self.unit = 'Bohr'
self.charge_model = 'point'
# Initialize ._atm and ._env to save the coordinates and charges and
# other info of MM particles
natm = len(atoms)
_atm = numpy.zeros((natm,6), dtype=numpy.int32)
_atm[:,gto.CHARGE_OF] = [charge(a[0]) for a in atoms]
coords = numpy.asarray([a[1] for a in atoms], dtype=numpy.double)
if charges is None:
_atm[:,gto.NUC_MOD_OF] = gto.NUC_POINT
charges = _atm[:,gto.CHARGE_OF:gto.CHARGE_OF+1]
else:
_atm[:,gto.NUC_MOD_OF] = gto.NUC_FRAC_CHARGE
charges = numpy.asarray(charges)[:,numpy.newaxis]
self._env = numpy.append(numpy.zeros(gto.PTR_ENV_START),
numpy.hstack((coords, charges)).ravel())
_atm[:,gto.PTR_COORD] = gto.PTR_ENV_START + numpy.arange(natm) * 4
_atm[:,gto.PTR_FRAC_CHARGE] = gto.PTR_ENV_START + numpy.arange(natm) * 4 + 3
if zeta is not None:
self.charge_model = 'gaussian'
zeta = numpy.asarray(zeta, dtype=float).ravel()
self._env = numpy.append(self._env, zeta)
_atm[:,gto.PTR_ZETA] = gto.PTR_ENV_START + natm*4 + numpy.arange(natm)
self._atm = _atm
self._built = True
[docs]
def get_zetas(self):
if self.charge_model == 'gaussian':
return self._env[self._atm[:,gto.PTR_ZETA]]
else:
return 1e16
[docs]
def create_mm_mol(atoms_or_coords, charges=None, radii=None, unit='Angstrom'):
'''Create an MM object based on the given coordinates and charges of MM
particles.
Args:
atoms_or_coords : array-like
Cartesian coordinates of MM atoms, in the form of a 2D array:
[(x1, y1, z1), (x2, y2, z2), ...]
Kwargs:
charges : 1D array
The charges of MM atoms.
radii : 1D array
The Gaussian charge distribution radii of MM atoms.
unit : string
The unit of the input. Default is 'Angstrom'.
'''
if isinstance(atoms_or_coords, numpy.ndarray):
# atoms_or_coords == np.array([(xx, xx, xx)])
# Patch ghost atoms
atoms = [(0, c) for c in atoms_or_coords]
elif (isinstance(atoms_or_coords, (list, tuple)) and
atoms_or_coords and
isinstance(atoms_or_coords[0][1], (int, float))):
# atoms_or_coords == [(xx, xx, xx)]
# Patch ghost atoms
atoms = [(0, c) for c in atoms_or_coords]
else:
atoms = atoms_or_coords
atoms = gto.format_atom(atoms, unit=unit)
if radii is None:
zeta = None
else:
radii = numpy.asarray(radii, dtype=float).ravel()
if not is_au(unit):
radii = radii / param.BOHR
zeta = 1 / radii**2
return Mole(atoms, charges=charges, zeta=zeta)
