MXtalTools: A Toolkit for Machine Learning on Molecular Crystals
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Abstract (arXiv)
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.
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References · 5
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
- 2025UMA: A Family of Universal Models for Atomscited by 105
- 2023Geometric Deep Learning for Molecular Crystal Structure Predictioncited by 3
- 2024Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and Posecited by 3
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