M3SOSCF Implementation

examples/scf/73-m3soscf.py

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+'''
+
+This file details the basic usage of the M3SOSCF algorithm.
+
+'''
+
+from pyscf import gto, scf, dft
+
+
+
+# Building the molecule, here CO is used as a simple example
+mol_1 = gto.M(atom='C 0.0 0.0 0.0; O 1.27 0.0 0.0', basis='6-31g', verbose=4)
+mol_2 = gto.M(atom='C 0.0 0.0 0.0; O 1.27 0.0 0.0', basis='6-31g', verbose=4, 
+              charge=1, spin=1)
+
+
+# First, a simple M3 iteration can be performed directly. The argument 'agents'
+# is positional and required. In this example, 5 agents are used
+m3_1 = scf.M3SOSCF(scf.RHF(mol_1), 5)
+
+# Alternatively, the M3 object can also be built via a decorator:
+#m3_1 = scf.RHF(mol_1).m3soscf(5)
+
+# The Iteration can be started both with the 'converge()' or 'kernel()'
+# function.
+conv, e_tot, mo_energy, mo_coeff, mo_occ = m3_1.kernel()
+
+print(f"Converged? {conv}\nTotal Energy: {e_tot} ha\nMO Occupancies: {mo_occ}")
+
+# This works identically for UHF
+m3_2 = scf.M3SOSCF(scf.UHF(mol_2), 5)
+conv, e_tot, mo_energy, mo_coeff, mo_occ = m3_2.kernel()
+
+print(f"Converged? {conv}\nTotal Energy: {e_tot} ha")
+
+# And it works identically for ROHF, RKS, UKS and ROKS
+m3_1 = scf.M3SOSCF(dft.RKS(mol_1, xc='blyp'), 5)
+conv, e_tot, mo_energy, mo_coeff, mo_occ = m3_1.kernel()
+print(f"Converged? {conv}\nTotal Energy: {e_tot} ha\nMO Occupancies: {mo_occ}")
+
+m3_2 = scf.M3SOSCF(dft.UKS(mol_2, xc='blyp'), 5)
+conv, e_tot, mo_energy, mo_coeff, mo_occ = m3_2.kernel()
+print(f"Converged? {conv}\nTotal Energy: {e_tot} ha")
+
+# When constructing the M3SOSCF object, the following optional arguments can 
+# be used:
+
+# purge_solvers: fraction of solvers that are to be removed and reassigned
+# every iteration
+purge_solvers = 0.5 # Default
+# convergence: 10^-convergence is the trust threshold for when an iteration 
+# is perceived as converged.
+convergence = 8 # Default
+# init_scattering: Initial random scattering of the solvers.
+init_scattering = 0.3 # Default
+# trust_scale_range: Three numbers that define the influence of trust on the 
+# scaling of the next iteration. The three numbers (gamma_1, gamma_2, gamma_3) 
+# are used in the formula:
+#
+#   1 / scale = (gamma_2 - gamma_1) * (1 - trust)^gamma_3 + gamma_1
+#
+# Therefore, they carray the role of (min, max, power)
+trust_scale_range = (0.5, 0.5, 0.05) # Default
+# init_guess: Initial Guess. This can either be a string that is documented 
+# in the SCF module (e. g. minao, 1e) or a numpy.ndarray of MO coeffs. In the 
+# latter case, the matrix is not modified or controlled prior to the SCF 
+# iteration, therefore incorrect symmetry will lead to incorrect results.
+init_guess = 'minao' # Default
+# stepsize: Stepsize of the parent CIAH solver
+stepsize = 0.2 # Default
+
+m3_1 = scf.M3SOSCF(scf.RHF(mol_1), 5, purge_solvers=purge_solvers, 
+                   convergence=convergence, init_scattering=init_scattering, 
+                   trust_scale_range=trust_scale_range, init_guess=init_guess, 
+                   stepsize=stepsize)
+conv, e_tot, mo_energy, mo_coeff, mo_occ = m3_1.kernel()
+print(f"Converged? {conv}\nTotal Energy: {e_tot} ha")
+