qcio 0.7.0

Beautiful and user friendly data structures for quantum chemistry.

Quantum Chemistry I/O

Beautiful and user friendly data structures for quantum chemistry.

Inspired by QCElemental. Built for consistency and rapid development.

qcio works in harmony with a suite of other quantum chemistry tools for fast, structured, and interoperable quantum chemistry.

The QC Suite of Programs

• qcio - Beautiful and user friendly data structures for quantum chemistry.
• qcparse - A library for efficient parsing of quantum chemistry data into structured qcio objects.
• qcop - A package for operating quantum chemistry programs using qcio standardized data structures. Compatible with TeraChem, psi4, QChem, NWChem, ORCA, Molpro, geomeTRIC and many more.
• BigChem - A distributed application for running quantum chemistry calculations at scale across clusters of computers or the cloud. Bring multi-node scaling to your favorite quantum chemistry program.
• ChemCloud - A web application and associated Python client for exposing a BigChem cluster securely over the internet.

Installation

pip install qcio

Quickstart

qcio is built around a simple mental model: Input objects are used to define inputs for a quantum chemistry program, and Output objects are used to capture the outputs from a quantum chemistry program.

All qcio objects can be serialized and saved to disk by calling .save("filename.json") and loaded from disk by calling .open("filename.json"). qcio supports json, yaml, and toml file formats. Binary data will be automatically base64 encoded and decoded when saving and loading.

Input Objects

ProgramInput - Core input object for a single QC program.

from qcio import Molecule, ProgramInput
# xyz files or saved Molecule objects can be opened from disk
caffeine = Molecule.open("caffeine.xyz")
# Define the program input
prog_input = ProgramInput(
    molecule=caffeine,
    calctype="energy",
    keywords={"purify": "no", "restricted": False},
    model={"method": "hf", "basis": "sto-3g"},
    extras={"comment": "This is a comment"}, # Anything extra not in the schema
)
# Binary or other files used as input can be added
prog_input.open_file("wfn.dat")
prog_input.keywords["initial_guess"] = "wfn.dat"

# Save the input to disk in json, yaml, or toml format
prog_input.save("input.json")

# Open the input from disk
prog_input = ProgramInput.open("input.json")

DualProgramInput - Input object for a workflow that uses multiple QC programs.

DualProgramInput objects can be used to power workflows that require multiple QC programs. For example, a geometry optimization workflow might use geomeTRIC to power the optimization and use terachem to compute the energies and gradients.

from qcio import Molecule, DualProgramInput
# xyz files or saved Molecule objects can be opened from disk
caffeine = Molecule.open("caffeine.xyz")
# Define the program input
prog_input = DualProgramInput(
    molecule=caffeine,
    calctype="optimization",
    keywords={"maxiter": 250},
    subprogram="terachem",
    subprogram_args = {
        "model": {"method": "hf", "basis": "sto-3g"},
        "keywords": {"purify": "no", "restricted": False},
    },
    extras={"comment": "This is a comment"}, # Anything extra not in the schema
)

FileInput - Input object for a QC programs using native file formats.

qcio also supports the native file formats of each QC program with a FileInput object. Assume you have a directory like this with your input files for psi4:

psi4/
    input.dat
    geometry.xyz
    wfn.dat

You can collect these native files and any associated command line arguments needed to specify a calculation into a FileInput object like this:

from qcio import FileInput
psi4_input = FileInput.from_directory("psi4")

# All input files will be loaded into the `files` attribute
psi4_input.files
# {'input.dat': '...', 'geometry.xyz': '...', 'wfn.dat': '...'}

# Add psi4 command line args to the input
psi4_input.cmdline_args.extend(["-n", "4"])

# Files can be dumped to a directory for a calculation
psi4_input.save_files("psi4")

Modifying Input Objects

Objects are immutable by default so if you want to modify an object cast it to a dictionary, make the desired modification, and then instantiate a new object. This prevents accidentally modifying objects that may already be referenced in other calculations--perhaps as .input_data on an Output object.

# Cast to a dictionary and modify
new_input_dict = prog_input.model_dumps()
new_input_dict["model"]["method"] = "b3lyp"
# Instantiate a new object
new_prog_input = ProgramInput(**new_input_dict)

Output Objects

Currently supported Output objects include SinglePointOutput for energy, gradient, and hessian calculations; and OptimizationOutput for optimization and transition state calculations. All Output objects have the same basic API:

output_obj.input_data # Input data used by the QC program
output_obj.success # Whether the calculation succeeded
output_obj.results # All structured results from the calculation
output_obj.stdout # Stdout log from the calculation
output_obj.files # Any files returned by the calculation
output_obj.provenance # Provenance information about the calculation
output_obj.extras # Any extra information not in the schema

The only difference between a SinglePointOutput and an OptimizationOutput is the results attribute. SinglePointOutput objects have a SinglePointResults object, and OptimizationOutput objects have an OptimizationResults object. Available attributes for each result type can be found by calling dir() on the object.

dir(output_obj.results)

Results can be saved to disk in json, yaml, or toml format by calling .save("filename.{json/yaml/toml}") and loaded from disk by calling .open("filename.{json/yaml/toml}").