Huang Guannan, Guo Yani, Chen Ye, Nie Zhengwei
School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211816, China.
Materials (Basel). 2023 Aug 31;16(17):5977. doi: 10.3390/ma16175977.
Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented.
材料创新在技术进步和产业发展中起着非常重要的作用。传统的实验探索和数值模拟通常需要大量的时间和资源。迫切需要一种新方法来加速新材料的发现和探索。机器学习可以大大降低计算成本，缩短开发周期，并提高计算精度。它已成为新型材料筛选和材料性能预测过程中最有前途的研究方法之一。近年来，机器学习已广泛应用于许多研究领域，如超导、热电、光伏、催化和高熵合金。在这篇综述中，简要概述了机器学习的基本原理。然后介绍了机器学习模型中几种常用的算法及其主要应用。讨论了机器学习在预测材料性能和指导材料合成方面的研究进展。最后，对材料科学领域中机器学习的未来发展进行了展望。
