EngineHelper

class optking.opt_helper.EngineHelper(optimization_input, **kwargs)

Bases: Helper

Perform an optimization using qcengine to compute properties. Use OptimizationInput to setup

a molecular system

calling compute() will perform a QCEngine calculation according to the provided QCInputSpecification.

calling optimize() after instantiation will perform an automatic optimization returning an OptimizationResult.

>>> import optking
>>> import qcengine as qcng

>>> from qcelemental.models import Molecule, OptimizationInput
>>> from qcelemental.models.common_models import Model
>>> from qcelemental.models.procedures import QCInputSpecification

>>> molecule = Molecule.from_data(
...     symbols = ["O", "O", "H", "H"],
...     geometry = [
...         0.0000000000,
...         0.0000000000,
...         0.0000000000,
...         -0.0000000000,
...         -0.0000000000,
...         2.7463569188,
...         1.3013018774,
...         -1.2902977124,
...         2.9574871774,
...         -1.3013018774,
...         1.2902977124,
...         -0.2111302586,
...     ],
...     fix_com=True,
...     fix_orientation=True,
... )

>>> model = Model(method="hf", basis="sto-3g")
>>> input_spec = QCInputSpecification(
...     driver="gradient", model=model, keywords={"d_convergence": 1e-7}  # QC program options
... )

>>> opt_input = OptimizationInput(
...     initial_molecule=molecule,
...     input_specification=input_spec,
...     keywords={"g_convergence": "GAU_TIGHT", "program": "psi4"},  # optimizer options
... )

>>> opt = optking.EngineHelper(opt_input)
>>> # opt_result = opt.optimize()  # optimize geometry - no interaction

>>> for step in range(30):
...    # Compute one's own energy and gradient
...    opt.compute() # process input. Get ready to take a step
...    opt.take_step()
...    conv = opt.test_convergence()
...    if conv is True:
...        print("Optimization SUCCESS:")
...        break
>>> else:
...     print("Optimization FAILURE:

“)

>>> json_output = opt.close() # create an unvalidated OptimizationOutput like object
>>> E = json_output["energies"][-1]

Methods Summary

from_dict(d)	Construct as far as possible the helper.
optimize()	Creating an EngineHelper and calling optimize() is equivalent to calling the deprecated optimize_qcengine() with an OptimizationInput.

Methods Documentation

classmethod from_dict(d)

Construct as far as possible the helper. Child class will need to update computer

optimize()

Creating an EngineHelper and calling optimize() is equivalent to calling the deprecated optimize_qcengine() with an OptimizationInput. However, EngineHelper will maintain its state.