Machine learning (ML) can optimize the research paradigm and shorten the time from discovery to application of novel functional materials, pharmaceuticals, and fine chemicals. Besides supporting material and drug design, ML is a potentially valuable tool for predictive modeling and process optimization. Herein, we first review the recent progress in data-driven ML for molecular crystal design, including property and structure predictions. ML can accelerate the development of the solvates, co-crystals, and colloidal nanocrystals, and improve the efficiency of crystal design. Next, this review summarizes ML algorithms for crystallization behavior prediction and process regulation. ML models support drug solubility prediction, particle agglomeration prediction, and spherical crystal design. ML-based in situ image processing can extract particle information and recognize crystal products. The application scenarios of ML algorithms utilized in crystallization processes and two control strategies based on supersaturation regulation and image processing are also presented. Finally, emerging techniques and the outlook of ML in drug molecular design and industrial crystallization processes are outlined.

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机器学习在分子晶体设计与结晶开发中的研究进展

机器学习 （ML） 可以优化研究范式，并缩短新型功能材料、药物和精细化学品从发现到应用的时间。除了支持材料和药物设计外，ML 还是预测建模和流程优化的潜在有价值的工具。在本文中，我们首先回顾了用于分子晶体设计的数据驱动 ML 的最新进展，包括性质和结构预测。ML 可以加速溶剂化物、共晶体和胶体纳米晶体的开发，并提高晶体设计的效率。接下来，本文总结了用于结晶行为预测和过程调控的 ML 算法。ML 模型支持药物溶解度预测、颗粒团聚预测和球形晶体设计。基于 ML 的原位图像处理可以提取颗粒信息并识别晶体产物。还介绍了结晶过程中使用的 ML 算法的应用场景以及基于过饱和度调节和图像处理的两种控制策略。最后，概述了 ML 在药物分子设计和工业结晶过程中的新兴技术和前景。