Skip to main content

MolCrysKit: Molecular Crystal Toolkit

License: MIT Python Version PyPI unit-tests

Overview

MolCrysKit is a Python toolkit designed for handling molecular crystals, providing utilities for parsing crystallographic data, identifying molecules within crystals, and performing various analyses on molecular crystals using graph theory and the Atomic Simulation Environment (ASE).

Key Features

  • Robust Molecule Identification: Identify individual molecules within a crystal structure using graph-based algorithms
  • Disorder Handling: Process disordered structures with graph algorithms
  • Topological Surface Generation: Create surface slabs while preserving molecular topology
  • Hydrogen Completion: Add hydrogen atoms with heuristic geometric placement rules
  • Cluster Carving: Disorder resolution → desolvation → coordination-shell cluster extraction
  • Nanocluster Carving: Build arbitrary finite shapes while preserving complete molecular or unit-cell topology
  • LLM-based AI agent Friendliness: Try on https://matmaster.bohrium.com/matmaster or build your own skill for your agent


Molecule Identification


Disorder Handling


Topological Surface Generation


Hydrogen Completion


Cluster Carving

Installation

pip install molcrys-kit

From source (development)

git clone https://github.com/SchrodingersCattt/MolCrysKit.git
cd MolCrysKit
pip install -e ".[dev]"

All dependencies are declared in pyproject.toml (there is no separate requirements.txt). requires-python = ">=3.10". The available extras are:

Extra Adds
[test] pytest, pytest-cov
[vis] nglview, py3Dmol for 3-D visualisation in notebooks
[dev] [test] + [vis] + build, ruff>=0.15, pre-commit, nbstripout, twine

So a contributor environment is pip install -e ".[dev]" and a CI / minimal test environment is pip install -e ".[test]".

Quick Start

Here's a simple example of how to use MolCrysKit:

import molcrys_kit as mck
from ase import Atoms

# 1. Create a toy system (e.g., 2 Water molecules in a unit cell)
# In practice, you would typically load this from a file: atoms = read('cif_file.cif')
atoms = Atoms(
    symbols=['O', 'H', 'H', 'O', 'H', 'H'],
    positions=[
        [1.0, 1.0, 1.0], [1.8, 1.0, 1.0], [0.7, 1.6, 1.0],  # Molecule 1
        [5.0, 5.0, 5.0], [5.8, 5.0, 5.0], [4.7, 5.6, 5.0]   # Molecule 2
    ],
    cell=[10.0, 10.0, 10.0],
    pbc=True
)

# 2. Initialize MolecularCrystal (Automatically identifies molecules via graph logic)
crystal = mck.MolecularCrystal.from_ase(atoms)

# 3. Access Crystal & Molecular Properties
print(f"Lattice Parameters: {crystal.get_lattice_parameters()}")
print(f"Identified Molecules: {len(crystal.molecules)}") 

mol = crystal.molecules[0]
print(f"Molecule 1 Formula: {mol.get_chemical_formula()}")
print(f"Molecule 1 Center of Mass: {mol.get_center_of_mass()}")

Citation

If you use MolCrysKit in academic work, please cite:

Guo, M.-Y.; Zhang, W.-X. MolCrysKit: A Topology-Aware Toolkit for Bridging Experimental Molecular-Crystal Structures and Simulation-Ready Modeling. J. Chem. Inf. Model. 2026, 66 (9), 4999-5007. https://doi.org/10.1021/acs.jcim.6c00168

For exact reproduction of the published JCIM results, use the archived v0.1.0 release together with the versioned container image and the material under paper/. The main branch may continue to evolve after publication.

Docker / Cloud

See Docker Guide for local Docker, Bohrium cloud deployment, and GHCR image publication.

Command Line Interface

Installing MolCrysKit also installs the mck command. The CLI is self-documenting; use --help at any level to see the exact arguments for your installed version:

mck --help
mck io --help
mck operate --help
mck analyze --help
mck operate cluster --help
mck operate nanocluster --help
mck operate void --help

The command groups roughly mirror the Python package layout:

  • mck io ... — inspect molecular-crystal contents, extract molecules, and convert structures (info, molecules, extract-molecule, convert).
  • mck operate ... — generate modified structures (disorder, disorder-supercell, add-h, slab, cluster, nanocluster, void, supercell, vacancy, desolvate, interpolate, reorient).
  • mck analyze ... — print analysis reports (summary, bfdh, interactions, polyhedra, sanity-check).

Documentation

You are… Start here
Using the library API & Capabilities · Tutorials
AI agent (using the library) API & Capabilities — read "Capability Map" then "Module Index"
AI agent (modifying code) AGENTS.md · Architecture
Docker / cloud Docker Guide

molcrys_kit/ — source code · scripts/ — diagnostic utilities · examples/ — CIF structure files

License

This project is licensed under the MIT License - see the LICENSE file for details.

Metadata

Release files for molcrys-kit 0.7.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for molcrys-kit 0.7.1
File Size Uploaded
molcrys_kit-0.7.1.tar.gz 5.8 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for molcrys-kit 0.7.1
File Interpreter ABI Platform
molcrys_kit-0.7.1-py3-none-any.whl Python 3 none any Details

Total release size: 6.2 MB

Release files / molcrys_kit-0.7.1.tar.gz

Download URL molcrys_kit-0.7.1.tar.gz
Size 5.8 MB
Tags Source
SHA-256 checksum
How to use checksums
0f0b597dd49200c15ed637a9016e3f833fa16e2746ae8ebcdb0500a004eaeea0
BLAKE2b-256 checksum
How to use checksums
f2d17701411221bb8be81b65040d3725606e27f8e8f994cfbb7fdda659b780ca
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 29, 2026.

Transparency log

Release files / molcrys_kit-0.7.1-py3-none-any.whl

Download URL molcrys_kit-0.7.1-py3-none-any.whl
Size 381.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
22d8b5c448cde51d18c77355e738d5ac34450d77c5986dbf1cc07df639bee977
BLAKE2b-256 checksum
How to use checksums
2b7345dc1dc632b68661fd4fbab8684b1e4d7911937695a4f321db2819cf02d5
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Aug 29, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.7.1 This release

2 release files

0.7.0

2 release files

0.6.1

2 release files

0.6.0

2 release files

0.5.2

2 release files

0.5.1

2 release files

0.5.0

2 release files

0.4.0

2 release files

0.3.0

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page