# -*- coding: utf-8 -*-
"""Global control parameters for Psi4
"""
import logging
import psi4_step
# import seamm
logger = logging.getLogger(__name__)
[docs]
class OptimizationParameters(psi4_step.EnergyParameters):
"""The control parameters for the energy."""
parameters = {
"optimization method": {
"default": "Rational Function Optimization",
"kind": "enumeration",
"default_units": "",
"enumeration": [*psi4_step.optimization_methods],
"format_string": "s",
"description": "Method:",
"help_text": "The optimization method to use.",
},
"max geometry steps": {
"default": "6*nAtoms",
"kind": "integer",
"default_units": "",
"enumeration": ("6*nAtoms",),
"format_string": "",
"description": "Maximum steps:",
"help_text": (
"The maximum number of steps to take in the optimization. "
"'default' is based on the system size, giving a reasonable "
"limit in most cases."
),
},
"geometry convergence": {
"default": "QChem",
"kind": "string",
"default_units": "",
"enumeration": [x for x in psi4_step.optimization_convergence],
"format_string": "",
"description": "Convergence criteria:",
"help_text": "The criteria to use for convergence.",
},
"recalc hessian": {
"default": "never",
"kind": "integer",
"default_units": "",
"enumeration": ("every step", "at beginning", "never"),
"format_string": "",
"description": "Recalculate Hessian:",
"help_text": (
"How often to recalculate the Hessian (in steps). Smaller "
"values help convergence but are expensive."
),
},
"hessian update": {
"default": "bfgs",
"kind": "enumeration",
"default_units": "",
"enumeration": (
"bfgs",
"ms",
"powell",
"none",
),
"format_string": "s",
"description": "Hessian update:",
"help_text": "The algorithm for updating the Hessian.",
},
"coordinates": {
"default": "Internal",
"kind": "enumeration",
"default_units": "",
"enumeration": (
"Internal",
"Cartesian",
"Both",
),
"format_string": "s",
"description": "Type of coordinates:",
"help_text": "The typ of coordinates to use in the minimization.",
},
}
def __init__(self, defaults={}, data=None):
"""Initialize the instance, by default from the default
parameters given in the class"""
super().__init__(
defaults={**OptimizationParameters.parameters, **defaults}, data=data
)
