def test_rhf_0d(self):
from pyscf.df import mdf_jk
from pyscf.scf import hf
L = 4
cell = pbcgto.Cell()
cell.build(
unit='B',
a=numpy.eye(3) * L * 5,
gs=[10] * 3,
atom='''He 2 2 2; He 2 2 3''',
dimension=0,
verbose=0,
basis={'He': [[0, (0.8, 1.0)], [0, (1.0, 1.0)], [0, (1.2, 1.0)]]})
mol = cell.to_mol()
mf = mdf_jk.density_fit(hf.RHF(mol))
mf.with_df.gs = [10] * 3
mf.with_df.auxbasis = {'He': [[0, (1e6, 1)]]}
mf.with_df.charge_constraint = False
mf.with_df.metric = 'S'
eref = mf.kernel()
mf = pbchf.RHF(cell)
mf.with_df = pdf.PWDF(cell)
mf.exxdiv = None
mf.get_hcore = lambda *args: hf.get_hcore(mol)
mf.energy_nuc = lambda *args: mol.energy_nuc()
e1 = mf.kernel()
self.assertAlmostEqual(e1, eref, 8)
def test_hf(pseudo=None):
# The molecular calculation
mol = gto.Mole()
mol.unit = 'B'
L = 60
mol.atom.extend([
['He', (L / 2., L / 2., L / 2.)],
])
# these are some exponents which are not hard to integrate
mol.basis = {'He': [[0, (0.8, 1.0)], [0, (1.0, 1.0)], [0, (1.2, 1.0)]]}
mol.build()
m = hf.RHF(mol)
print "Molecular HF energy"
print(m.scf()) # -2.63502450321874
# The periodic calculation
cell = pbcgto.Cell()
cell.unit = 'B'
cell.h = np.diag([L, L, L])
cell.gs = np.array([60, 60, 60])
cell.nimgs = [0, 0, 0]
cell.atom = mol.atom
cell.basis = mol.basis
cell.pseudo = pseudo
# cell.verbose = 4
cell.build(None, None)
mf = pbchf.RHF(cell)
print(mf.scf()) # -2.58766850182551: doesn't look good, but this is due
def mf_initializer_diis(mol):
"""Will init pyscf hf engine. With damping of 0.3 and maximum of 100
iterations"""
mf = hf.RHF(mol)
mf.verbose = 1
mf.max_cycle = 100
return mf
def measure_hf_energy(p_batch, molecules):
energies = []
for (p, mol) in zip(p_batch, molecules):
mf = hf.RHF(mol.get_pyscf_molecule())
h1e = mf.get_hcore()
veff = mf.get_veff(dm=p.astype("float64"))
energies.append(mf.energy_tot(p.astype("float64"), h1e, veff))
return energies
def scf_runs(molecules):
S, P = [], []
for i, molecule in enumerate(molecules):
msg.info(str(i + 1) + "/" + str(len(molecules)))
mol = molecule.get_pyscf_molecule()
mf = hf.RHF(mol)
mf.verbose = 1
mf.run()
S.append(mf.get_ovlp().reshape((dim**2, )))
P.append(mf.make_rdm1().reshape((dim**2, )))
return S, P
def RHF(mol, *args):
'''This is a wrap function to decide which SCF class to use, RHF or ROHF
'''
if mol.nelectron == 1:
if mol.symmetry:
return rhf_symm.HF1e(mol)
else:
return rohf.HF1e(mol)
elif not mol.symmetry or mol.groupname is 'C1':
if mol.spin > 0:
return rohf.ROHF(mol, *args)
else:
return rhf.RHF(mol, *args)
else:
if mol.spin > 0:
return rhf_symm.ROHF(mol, *args)
else:
return rhf_symm.RHF(mol, *args)
def scf_runs(molecules):
S, P, F = [], [], []
for i, molecule in enumerate(molecules):
msg.info(str(i + 1) + "/" + str(len(molecules)))
mol = molecule.get_pyscf_molecule()
mf = hf.RHF(mol)
mf.verbose = 1
mf.run()
h = mf.get_hcore(mol)
s = mf.get_ovlp()
p = mf.make_rdm1()
f = mf.get_fock(h, s, mf.get_veff(mol, p), p)
S.append(s.reshape((dim**2, )))
P.append(p.reshape((dim**2, )))
F.append(f.reshape((dim**2, )))
return S, P, F
def not_used():
msg.info("Netowrk Analysis!", 2)
#--- fetching the molecules ---
msg.info("Fetching the molecules", 2)
def grep_molecule(input_file):
import re
with open(input_file) as f:
molecule = re.search(r"\$molecule.*\$end", f.read(), re.DOTALL)
if molecule is None:
raise ValueError("No molecule found in " + f.name)
else:
molecule = molecule.group(0)
# cut out geometries
geometries = molecule.splitlines()[2:-1]
# from geometries take the species and positions
species, positions = [], []
for line in geometries:
splits = line.split()
species.append(splits[0])
positions.append(splits[1:])
return species, positions
def fetch_molecules(folder):
files = [file for file in listdir(folder) if ".inp" in file]
for i, file in enumerate(files):
msg.info("Fetching: " + str(i + 1) + "/" + str(len(files)))
mol = Molecule(*grep_molecule(join(folder, file)))
mol.basis = "sto-3g"
yield mol
molecules = list(fetch_molecules("butadien/data"))
#---
#--- do scf ---
msg.info("Running the SCF calculations", 2)
iterations = []
for i, molecule in enumerate(molecules):
mol = molecule.get_pyscf_molecule()
msg.info("Calculating: " + str(i + 1) + "/200.")
# assemble pyscf initial guesses
P_1e = hf.init_guess_by_1e(mol)
P_atom = hf.init_guess_by_atom(mol)
P_minao = hf.init_guess_by_minao(mol)
# nn guess
S = hf.get_ovlp(mol).reshape(1, dim**2)
P_NN = network.run(sess, S).reshape(dim, dim)
iterations_molecule = []
for guess in [P_1e, P_atom, P_minao, P_NN]: #, P_NN]:
mf = hf.RHF(mol)
mf.verbose = 1
mf.kernel(dm0=guess)
iterations_molecule.append(mf.iterations)
iterations.append(iterations_molecule)
iterations = np.array(iterations)
#---
#--- statistics ---
fig, axes = plt.subplots(2, 2)
bins = 1 # todo hier kann man auch ein array angeben
for i, name in enumerate(['1e', 'atom', 'P_minao']):
hist, bins = np.histogram(iterations[:, i])
center = (bins[:-1] + bins[1:]) / 2
axes[i].bar(center, hist, label=name)
plt.legend()
plt.show()
# A tag to label the derived SCF class
class _X2C_SCF:
pass
if __name__ == '__main__':
mol = mole.Mole()
mol.build(
verbose=0,
atom=[["O", (0., 0., 0.)], [1, (0., -0.757, 0.587)],
[1, (0., 0.757, 0.587)]],
basis='ccpvdz-dk',
)
method = hf.RHF(mol)
enr = method.kernel()
print('E(NR) = %.12g' % enr)
method = UHF(mol)
ex2c = method.kernel()
print('E(X2C1E) = %.12g' % ex2c)
method.with_x2c.basis = {'O': 'unc-ccpvqz', 'H': 'unc-ccpvdz'}
print('E(X2C1E) = %.12g' % method.kernel())
method.with_x2c.approx = 'atom1e'
print('E(X2C1E) = %.12g' % method.kernel())
method = UKS(mol)
ex2c = method.kernel()
print('E(X2C1E) = %.12g' % ex2c)
dm_ll = reduce(numpy.dot, (proj, dm_nr*.5, proj.T.conj()))
dm_ll = (dm_ll + dhf.time_reversal_matrix(mol, dm_ll)) * .5
return dm_ll
if __name__ == '__main__':
mol = mole.Mole()
mol.build(
verbose = 0,
atom = [["O" , (0. , 0. , 0.)],
[1 , (0. , -0.757 , 0.587)],
[1 , (0. , 0.757 , 0.587)] ],
basis = 'ccpvdz-dk',
)
method = hf.RHF(mol)
enr = method.kernel()
print('E(NR) = %.12g' % enr)
method = sfx2c1e(hf.RHF(mol))
esfx2c = method.kernel()
print('E(SFX2C1E) = %.12g' % esfx2c)
method.with_x2c.basis = 'unc-ccpvqz-dk'
print('E(SFX2C1E) = %.12g' % method.kernel())
method.with_x2c.approx = 'atom1e'
print('E(SFX2C1E) = %.12g' % method.kernel())
method = UHF(mol)
ex2c = method.kernel()
print('E(X2C1E) = %.12g' % ex2c)
method.with_x2c.basis = {'O': 'unc-ccpvqz', 'H':'unc-ccpvdz'}
def mf_initializer(mol):
mf = hf.RHF(mol)
mf.diis = None
mf.verbose = 1
return mf
P_atom = hf.init_guess_by_atom(mol)
P_minao = hf.init_guess_by_minao(mol)
print("die normalen sind fertig")
# nn guess
S = hf.get_ovlp(mol).reshape(1, dim**2)
P_NN = network.run(sess, S).reshape(dim, dim).astype('float64')
print("P_NN fertig")
iterations_molecule = []
for guess in [P_1e, P_atom, P_minao, P_NN]:#, P_NN]:
print("SCF")
mf = hf.RHF(mol)
mf.verbose = 1
mf.kernel(dm0=guess)
iterations_molecule.append(mf.iterations)
print("SCFs done")
iterations.append(iterations_molecule)
print("Next step")
iterations = np.array(iterations)
#---
#--- statistics ---
