We present MXtalTools, a flexible Python package for the data-driven modelling of molecular crystals, facilitating machine learning studies of the molecular solid state. MXtalTools comprises several classes of utilities: (1) synthesis, collation, and curation of molecule and crystal datasets, (2) integrated workflows for model training and inference, (3) crystal parameterization and representation, (4) crystal structure sampling and optimization, (5) end-to-end differentiable crystal sampling, construction and analysis. Our modular functions can be integrated into existing workflows or combined and used to build novel modelling pipelines. MXtalTools leverages CUDA acceleration to enable high-throughput crystal modelling. The Python code is available open-source on our GitHub page, with detailed documentation on ReadTheDocs.
@article{arxiv.2511.20327,
title = {MXtalTools: A Toolkit for Machine Learning on Molecular Crystals},
author = {Michael Kilgour and Mark E. Tuckerman and Jutta Rogal},
journal= {arXiv preprint arXiv:2511.20327},
year = {2025}
}