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Crystals are integral to a variety of industrial applications, such as the development of pharmaceuticals and advancements in material science. To anticipate crystal behavior and pinpoint effective crystallization techniques, a thorough investigation of crystal structures, properties, and the associated processes is essential. However, conventional methods like experimental procedures and quantum mechanics calculations, while crucial, can be expensive and time-consuming. In response, machine learning has risen as an effective alternative, complementing the traditional approaches based on quantum mechanics and classical force fields. In the recent years, the deployment of machine learning in the realm of crystallization has yielded notable progress. This review offers a concise overview of the application of machine learning techniques in crystallization, focusing on the past five years. Our analysis of the literature indicates that machine learning has accelerated the prediction of crystal structures by streamlining the generation and evaluation of structures. Additionally, it has facilitated the prediction of key crystal properties such as solubility, melting point, and habit. The review further explores the role of machine learning in refining the control and optimization of crystallization processes, highlighting the restrictions of conventional algorithms and sensing technologies. The advantages of end-to-end processing for enhancing the accuracy of predictions and the combination of data-driven with mechanism-based models for robustness are also considered. In summary, this review provides insights into the current state of machine learning in the field of intelligent crystallization and suggests pathways for future research and development.
Lu et al. (Thu,) studied this question.