About

optking is a molecular geometry optimization program that began as a rewrite of the previous c++ module of the same name in psi4. The translation into python and subsequent development was undertaken to support more complex tasks, and to join with interoperability efforts such as QCSchema, QCArchive and QCDB. optking supports the location of minima, transition states, and reaction paths on molecular potential surfaces. Current work is focused especially on expanding the available reaction path methods. Contributions are welcome! Please fork this repository and open a PR!

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Getting Started

Installation and Setup

The recommended method of installation is through conda. To install:

conda install optking -c conda-forge

The project is hosted on github Source code can be downloaded with git or with the tarballs provided under releases.

To install from source make sure all dependencies are installed via conda or pip and run:

pip install -e .

from the installation directory.

For most use cases, QCEngine and your QC/MM program of choice OR Psi4 is required. If using QCEngine see MolSSI's qcengine documentation to ensure proper setup. Any QC or MM programs will need to be installed such that QCEngine can find them.

To run optking without QCEngine or Psi4, the CustomHelper class may be used though the python API. This Helper allows for the use of arbitrary packages and/or modified gradients to be used. Alternatively, an OptimizationManager or an even lower-level class could be used with custom gradients. Gradients, energies, and possibly hessians can be provided directly. To use the most basic representation of the algorithms with no reference to molecules one of the classes inheriting from OptimizationAlgorithm will be needed.

Running through QCEngine

A basic driver has been implemented in QCEngine. QCEngine is built upon QCElemental which provides input validation and standardized input/output. To see the requirements for an Optimziation Input check MolSSI's qcelemental documentation. NOTE QCElemental assumes atomic units by default:

import qcengine as qcng

opt_input = {
    "initial_molecule": {
        "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,
    },
    "input_specification": {
        "model": {"method": "hf", "basis": "sto-3g"},
        "driver": "gradient",
        "keywords": {"d_convergence": "1e-7"},
    },
    "keywords": {"g_convergence": "GAU_TIGHT", "program": "psi4"},
}

result = qcng.compute_procedure(opt_input, "optking")

An explicit example of creating and running an OptimizationInput. NOTE Molecule.from_data seems to be the only place Angstroms are expected:

import qcengine as qcng

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

# WARNING. The user MUST set fix_com and fix_orientation to True.
# optimization will almost certainly fail otherwise
molecule = Molecule.from_data(
    """
    O        0.0000000000      0.0000000000      0.0000000000
    O       -0.0000000000     -0.0000000000      1.4533095991
    H        0.6886193476     -0.6827961938      1.5650349285
    H       -0.6886193476      0.6827961938     -0.1117253294""",
    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
)

config = qcng.get_config()  # get machine info (e.g. number of cores) can specify explicitly
opt = qcng.get_procedure("optking")
result = opt.compute(opt_input, config)

Running through Psi4 - Development

Direct integration in Psi4 is in development. Check an upcoming Psi4 release to run optking through psi4. Running this input file psi4 input.dat will trigger (as of 1.6) the old c++ optimizer. In the future this will trigger optimization through pyoptking. Almost everything in Psi4's current optking documentation is also applicable to the new optimizer. Optimizations can also be run through Psi4's python API.

molecule hooh {
    0 1
    O        0.0000000000      0.0000000000      0.0000000000
    O       -0.0000000000     -0.0000000000      1.4533095991
    H        0.6886193476     -0.6827961938      1.5650349285
    H       -0.6886193476      0.6827961938     -0.1117253294
}

set {
    d_convergence 1e-7
    g_convergence GAU_TIGHT
}

optimize("hf/sto-3g")

The OptHelpers

For users looking to run optimizations from python, an example of QCEngine's python API has already been shown. To run optking through Psi4's python API checkout the Psi4 API docs. These two options should be sufficient for the majority of users.

If direct control over the optimizer is desired two OptHelper classes are provided to streamline performing an optimization. The molecular system, optimization coordinates, history, etc are all accessible through their respective classes and may be accessed as attributes of the OptHelper instance. EngineHelper takes an OptimizationHelper and calls qcengine.compute() to perform basic calculations with the provided input_specification CustomHelper accepts QCElemental and Psi4 molecules while requiring user provided gradients, energies, and possibly hessians. This may be useful for implementing a custom optimization driver or procedure using optking.

EngineHelper:

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(
    """
    O        0.0000000000      0.0000000000      0.0000000000
    O       -0.0000000000     -0.0000000000      1.4533095991
    H        0.6886193476     -0.6827961938      1.5650349285
    H       -0.6886193476      0.6827961938     -0.1117253294""",
    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)

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:")
    else:
        print("Optimization FAILURE:\n")

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

CustomHelper can take psi4 or qcelemental molecules. A simple example of a custom optimization loop is shown where the gradients are provided from a simple lennard jones potential:

h2o = psi4.geometry(
"""
 O
 H 1 1.0
 H 1 1.0 2 104.5
"""
)

psi4_options = {
    "basis": "sto-3g",
}
optking_options = {"g_convergence": "gau_verytight", "intrafrag_hess": "SIMPLE"}

psi4.set_options(psi4_options)

opt = optking.CustomHelper(h2o, optking_options)

for step in range(30):

    # Compute one's own energy and gradient
    E, gX = optking.lj_functions.calc_energy_and_gradient(opt.geom, 2.5, 0.01, True)
    # Insert these values into the 'user' computer.
    opt.E = E
    opt.gX = gX

    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:\n")

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