开源工具包，助力分子晶体机器学习建模
MXtalTools: A Toolkit for Machine Learning on Molecular Crystals
- 模块化工具集，支持晶体数据构建与模型训练
- 支持端到端可微分晶体采样与结构优化
- 适合材料、化学领域研究者快速搭建新模型
我们提出MXtalTools，一个用于分子晶体数据驱动建模的灵活Python工具包，支持分子固态的机器学习研究。该工具包包含五大功能模块：（1）分子与晶体数据的合成、整理与清洗；（2）模型训练与推理的集成工作流；（3）晶体参数化与表征；（4）晶体结构采样与优化；（5）端到端可微分的晶体采样、构建与分析。其模块化设计可嵌入现有流程或组合构建新型建模管道。通过CUDA加速，实现高通量晶体建模。代码开源，托管于GitHub，配套文档详尽，见ReadTheDocs。
原文摘要 · Abstract (English)
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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