Year: 2026
Type: article
Abstract: We present MXtalTools, a flexible Python package for the data-driven modeling 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 data sets, (2) integrated workflows for model training and inference, (3) crystal parametrization 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 modeling pipelines. MXtalTools leverages CUDA acceleration to enable high-throughput crystal modeling. The Python code is available open-source on our GitHub page, with detailed documentation on ReadTheDocs.
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Awards: Strukturelle Phasenumwandlungen in kondensierter Materie Charakteristische Eigenschaften und vereinigende Konzepte für atomare Prozesse in atomaren und molekularen Festkörpern, DMREF: Accelerated discovery of metastable but persistent contact insecticide crystal polymorphs for enhanced activity and sustainability, Collaborative Research:CDS&E:D3SC:Topology, Rare-event Simulation, and Machine Learning as Routes to Predicting Molecular Crystal Structures and Understanding Their Phase Behavior.
OpenAlex ID: W7140198489