As the big data generated by the development of modern experiments and computing technology becomes more and more accessible, the material design method based on machine learning (ML) has opened a new paradigm for materials science research. With its ability to automatically solve complex tasks, machine learning is being used as a new method to help discover the relevance of materials, understand materials' properties, and accelerate the discovery of materials. This paper first introduces the general process of machine learning in materials science. Secondly, the applications of machine learning in material properties prediction, classification and identification, auxiliary micro-scale characterization, phase transformation research and phase diagram construction, process optimization, service behavior evaluation, accelerating the development of computational simulation technology, multi-objective optimization and inverse design of materials are reviewed. Finally, we discuss the main challenges and possible solutions in machine learning, and predict the potential research directions.

随着现代实验和计算技术的发展产生的大数据变得越来越容易获取，基于机器学习（ML）的材料设计方法为材料科学研究开辟了新的范式。凭借其自动解决复杂任务的能力，机器学习正被用作一种新方法来帮助发现材料的相关性、了解材料的特性并加速材料的发现。本文首先介绍了材料科学中机器学习的一般过程。其次，机器学习在材料性能预测、分类和识别、辅助微观表征、相变研究和相图构建、工艺优化、服务行为评估、对加速计算模拟技术的发展、材料的多目标优化和逆向设计进行了综述。最后，我们讨论了机器学习中的主要挑战和可能的解决方案，并预测了潜在的研究方向。