Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

中文翻译：

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人工智能和机器学习算法在结晶中的应用

人工智能，特别是机器学习应用程序如今被用于各种科学应用程序和尖端技术，它们具有变革性的影响。这种利用大数据集的统计和线性代数方法组合正越来越多地集成到化学和结晶研究工作流程中。本综述旨在首次全面概述机器学习和化学信息学应用，作为一种新的、强有力的手段来加速发现新的晶体结构、预测有机晶体材料的关键特性、模拟、理解和控制复杂结晶过程系统的动力学，以及有助于涉及结晶材料的化学过程开发的高通量自动化。我们批判性地回顾了这些新的、迅速出现的研究领域的进展，提高了人们对机器学习模型与第一原理机械模型、数据集大小、结构和质量的桥接等问题的认识，以及适当描述符的选择. 同时，我们提出了应用数学、化学和晶体学接口的未来研究。总体而言，本次审查旨在增加工业和学术界的化学家和科学家对此类方法和工具的采用。我们建议在应用数学、化学和晶体学的界面进行未来的研究。总体而言，本次审查旨在增加工业和学术界的化学家和科学家对此类方法和工具的采用。我们建议在应用数学、化学和晶体学的界面进行未来的研究。总体而言，本次审查旨在增加工业和学术界的化学家和科学家对此类方法和工具的采用。