# Copyright 2014-2020 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: Oliver Backhouse <olbackhouse@gmail.com>
# George Booth <george.booth@kcl.ac.uk>
#
'''
Functions for tuning the chemical potential.
'''
import numpy as np
from scipy import optimize
from pyscf import lib
from pyscf.lib import logger
from pyscf import __config__
def _objective(x, se, fock, nelec, occupancy=2, buf=None):
''' Objective function for the minimization
'''
w, v = se.eig(fock, chempot=x, out=buf)
chempot, error = binsearch_chempot((w,v), se.nphys, nelec,
occupancy=occupancy)
return error**2
def _gradient(x, se, fock, nelec, occupancy=2, buf=None):
''' Gradient function for the minimization
'''
w, v = se.eig(fock, chempot=x, out=buf)
chempot, error = binsearch_chempot((w,v), se.nphys, nelec,
occupancy=occupancy)
nocc = np.sum(w < chempot)
nphys = se.nphys
h1 = -np.dot(v[nphys:,nocc:].conj().T, v[nphys:,:nocc])
zai = -h1 / lib.direct_sum('i,a->ai', w[:nocc], -w[nocc:])
c_occ = np.dot(v[:nphys,nocc:], zai)
d_rdm1 = np.dot(v[:nphys,:nocc], c_occ.conj().T) * 4
ne = np.trace(d_rdm1).real
d = occupancy * error * ne
return error**2, d
[docs]
def binsearch_chempot(fock, nphys, nelec, occupancy=2):
''' Finds a chemical potential which best agrees with the number
of physical electrons and abides by the Aufbau principal via
a binary search.
Args:
fock : 2D array or tuple of arrays
Fock matrix to diagonalise, may be the physical Fock matrix
or extended Fock matrix. Can also be the output of
:func:`np.linalg.eigh` for this matrix, i.e. a tuple of the
eigenvalues and eigenvectors.
nphys : int
Number of physical degrees of freedom
nelec : int
Number of physical electrons
Kwargs:
occupancy : int
Occupancy of the states, i.e. 2 for RHF and 1 for UHF.
Default 2.
Returns:
chemical potential, and the error in the number of electrons
'''
if isinstance(fock, tuple):
w, v = fock
else:
w, v = np.linalg.eigh(fock)
nmo = v.shape[-1]
sum0 = sum1 = 0.0
for i in range(nmo):
n = occupancy * np.dot(v[:nphys,i].conj().T, v[:nphys,i]).real
sum0, sum1 = sum1, sum1 + n
if i > 0:
if sum0 <= nelec and nelec <= sum1:
break
if abs(sum0 - nelec) < abs(sum1 - nelec):
homo = i-1
error = nelec - sum0
else:
homo = i
error = nelec - sum1
lumo = homo+1
chempot = 0.5 * (w[homo] + w[lumo])
return chempot, error
[docs]
def minimize_chempot(se, fock, nelec, occupancy=2, x0=0.0, tol=1e-6, maxiter=200, jac=True):
''' Finds a set of auxiliary energies and chemical potential on
the physical space which best satisfy the number of electrons.
Args:
se : AuxiliarySpace
Auxiliary space
fock : 2D array
phys : 2D array
Physical space (1p + 1h), typically the Fock matrix
nelec : int
Number of physical electrons
Kwargs:
occupancy : int
Occupancy of the states, i.e. 2 for RHF and 1 for UHF.
Default 2.
x0 : float
Initial guess for :attr:`chempot`. Default 0.0
tol : float
Convergence threshold (units are the same as :attr:`nelec`).
Default 1e-6.
maxiter : int
Maximum number of iterations. Default 200.
jac : bool
If True, use gradient. Default True.
Returns:
AuxiliarySpace object with altered :attr:`energy` and
:attr:`chempot`, and the SciPy :attr:`OptimizeResult` object.
'''
tol = tol**2 # we minimize the squared error
dtype = np.result_type(se.energy.dtype, se.coupling.dtype, fock.dtype)
buf = np.zeros((se.nphys+se.naux, se.nphys+se.naux), dtype=dtype)
fargs = (se, fock, nelec, occupancy, buf)
options = {'maxiter': maxiter, 'ftol': tol, 'xtol': tol, 'gtol': tol}
kwargs = {'x0': x0, 'method': 'TNC', 'jac': jac, 'options': options}
fun = _objective if not jac else _gradient
opt = optimize.minimize(fun, args=fargs, **kwargs)
se.energy -= opt.x
se.chempot = binsearch_chempot(se.eig(fock), se.nphys, nelec,
occupancy=occupancy)[0]
return se, opt
