MolCrysKit: Molecular Crystal Toolkit

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

Installation

From PyPI (recommended)

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:

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.

Running with Docker

Two Dockerfiles are provided: Dockerfile (python:3.10-slim) for local use and Dockerfile.bohrium for the Bohrium cloud platform. Both install MolCrysKit from the GitHub archive.

git clone https://github.com/SchrodingersCattt/MolCrysKit.git
cd MolCrysKit
docker build -t molcryskit:latest .
docker run --rm molcryskit:latest python /opt/molcryskit/scripts/docker_smoke_test.py
docker run -it --rm -p 8888:8888 molcryskit:latest  # Jupyter

For Bohrium deployment, GHCR image publication, and mounting custom data, see the Docker Guide.

Documentation

Disorder Handling

MolCrysKit resolves crystallographic disorder through two complementary paths: the explicit path processes CIF _atom_site_disorder_assembly / _disorder_group tags (e.g. SHELXL PART groups), while the implicit SP path handles partial-occupancy atoms on special positions without disorder tags (SHELX riding-H refinements). A motif-merge post-pass reconstructs isolated XH_n centres (NH4+, H2O), and three replica-generation modes (optimal, random, enumerate) support downstream ensemble workflows. Valence-completeness diagnostics flag incomplete H-shells automatically.

For the full three-phase pipeline, edge-type priority table, solver modes, and symmetry-copy decoupling details, see Architecture.

Documentation Index

Project Structure

See the molcrys_kit/ directory for source code and the scripts/ directory for utility scripts (e.g. disorder diagnostics, molecule identification, CIF processing).

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

License

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