Solvation models#
Modules: pyscf.solvent
Introduction#
Solvation model allows the quantum chemistry calculations to include the interactions between solvents and the quantum solute. Solvents can be treated implicitly, known as continuum solvents, and explicitly. For continuum solvents, we implemented the PCM (polarizable continuum model), ddCOSMO (domain-decomposition COSMO solvation model), ddPCM (domain-decomposition polarizable continuum model), and SMD (Solvation Model Density). For the explicit solvent environment, we provided the interface to call the polarizable embedding library CPPE.
PCM model#
PySCF support four types of PCM solvent models, i.e. C-PCM, IEF-PCM, SS(V)PE, and COSMO (See https://manual.q-chem.com/5.2/Ch12.S2.SS2.html for more detailed descriptions of these methods). The analytical gradient and semi-analytical Hessian are also supported.
PCM solvent models can be applied on to an SCF object:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
mf = mol.RKS(xc='b3lyp').PCM()
mf.with_solvent.method = 'IEF-PCM' # C-PCM, SS(V)PE, COSMO
mf.with_solvent.eps = 78.3553 # for water
mf.run()
In regular MCSCF (CASCI or CASSCF), and post-SCF calculations, the setup for self-consistent solvent is similar:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
mc = mol.CASCI(4, 4).PCM().run()
mp2_model = mol.MP2().PCM().run()
Solvent for excited states#
When combining to TDDFT or other methods of excited states, solvent can be modelled in the manner of self-consistency (fast solvent) or single shot (slow solvent). Below we use TDDFT to demonstrate the treatments of fast solvent and slow solvent.
In vertical excitations, the solvent almost does not respond to the change of electronic structure. It should be viewed as slow solvent. The calculation can be started with an SCF with fully relaxed solvent and followed by a regular TDDFT method:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
mf = mol.RHF().PCM().run()
td = mf.TDA().run()
In the diabatic excitations, we would like to let the solvent rapidly responds to the electronic structure of excited states. The entire solvation system should converge to an equilibrium state between solvent and the excited state of the solute. In this scenario, solvent model should be applied on to the excited state methods:
mf = mol.RHF().PCM().run()
td = mf.TDA().PCM()
td.with_solvent.equilibrium_solvation = True
td.kernel()
Please note that the flag equilibrium_solvation needs to be set to True
in this case. PySCF by default assumes the slow solvent model for TDDFT.
In the complicated procedure which involves for example electronic states from different states (typically in the MCSCF calculations with state-average or state-specific approximations), PySCF PCM implementation allows to input a density matrix and freeze the solvent equilibrated against the input density matrix:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
mc = mol.CASCI(4, 4).PCM()
mc.fcisolver.nstates = 5
mc.with_solvent.state_id = 1 # Slow solvent wrt the first excited state
mc.run()
The slow solvent does not have to be corresponding to a particular state. It can be even the solvent from a different geometry or an artificial quantum state of solute:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
scf_dm = mol.RHF().run().make_rdm1()
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 1.035 -1.082
H 0. -1.035 -1.082''',
basis='6-31g*', verbose=4)
mc = mol.CASCI(4, 4).PCM(dm=scf_dm).run()
Solvent parameters#
The default solvent in the PCM module is water. When studying other types of
solvents, you can consider to modify the dielectric parameter eps using the
constants listed below:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
mf = mol.RHF().PCM()
mf.with_solvent.eps = 32.613 # methanol
mf.run()
These dielectric constants are obtained from https://gaussian.com/scrf/. More dataset can be found in Minnesota Solvent Descriptor Database (https://comp.chem.umn.edu/solvation)
ddCOSMO#
Self-consistent solvents for ground state#
Solvent model can be applied on to an SCF object:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
mf = mol.RKS(xc='b3lyp').DDCOSMO().run()
SMD model#
SMD model is recommended for computing solvation free energy. The implementation of SMD model in PySCF is based on IEF-PCM. Other SMx models are not supported yet (See https://manual.q-chem.com/5.2/Ch12.S2.SS8.html). The source code for CDS contribution is taken from NWChem (nwchemgit/nwchem) SMD solvent solvent models can be applied on to an SCF object:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
mf = mol.RKS(xc='b3lyp').SMD()
mf.with_solvent.solvent = 'water'
mf.run()
The solvent descriptor can be assigned to the with_solvent directly:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
mf = mol.RKS(xc='b3lyp').SMD()
mf.with_solvent.solvent_descriptors = [1.3843, 1.3766, 0.0, 0.45, 35.06, 13.45, 0.0, 0.0]
mf.run()
The format of solvant names are the same as Minnesota Solvent Descriptor Database
(https://comp.chem.umn.edu/solvation/mnsddb.pdf). One can assign solvent descriptors in the format
mf.with_solvent.solvent_descriptors = [n, n25, alpha, beta, gamma, epsilon, phi, psi]
Polarizable embedding#
To use polarizable embedding model for mean-field calculations, one would need to first generate potential data for the input of CPPE library. The best way to generate potential files is with PyFraME. You can directly throw in a pdb file, select the QM region and how to parametrize different parts of the environment (with either pre-defined potentials, or with LoProp). Some guidance is also provided in the Tutorial Review paper about PE, section 4: https://onlinelibrary.wiley.com/doi/full/10.1002/qua.25717 Therein, the format of the potential file is also explained (it’s the same format as used in the original Dalton pelib implementation).
With the generated potential file, one can carry out the polarizable embedding calculations:
import pyscf
mol = pyscf.M(atom='''
C 0. 0. -0.542
O 0. 0. 0.677
H 0. 0.935 -1.082
H 0. -0.935 -1.082''',
basis='6-31g*', verbose=4)
mf = mol.RKS(xc='b3lyp')
mf = pyscf.solvent.PE(mf, 'potfile')
mf.run()