Innovative Materials Science via Machine Learning
Chaochao Gao, Xin Min, Minghao Fang, Tianyi Tao, Xiaohong Zheng, Yangai Liu, Xiaowen Wu, Zhaohui Huang
Abstract
Abstract Nowadays, the research on materials science is rapidly entering a phase of data‐driven age. Machine learning, one of the most powerful data‐driven methods, have been being applied to materials discovery and performances prediction with undoubtedly tremendous application foreground. Herein, the challenges and current progress of machine learning are summarized in materials science, the design strategies are classified and highlighted, and possible perspectives are proposed for the future development. It is hoped this review can provide important scientific guidance for innovating materials science and technology via machine learning in the future.