机器学习
计算机科学
人工智能
领域(数学)
过程(计算)
财产(哲学)
哲学
数学
认识论
纯数学
操作系统
作者
G. S. Huang,Yani Guo,Ye Chen,Zhengwei Nie
出处
期刊:Materials
[MDPI AG]
日期:2023-08-31
卷期号:16 (17): 5977-5977
被引量:2
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI