摘要
概述了4种机器学习方法,包括监督学习、无监督学习、深度学习、强化学习。讨论了机器学习在材料设计与发现、材料表征和计算材料学中的具体应用,展示了其在加速材料开发和优化方面的潜力。介绍了材料科学中的数据库和数据挖掘技术,总结了数据库的发展和数据挖掘的应用。汇总了新兴大模型技术在材料科学中的应用,提出大模型技术的发展引领材料科学进入了智能化新时代。然而当前领域仍面临诸多挑战,如数据质量、模型解释性和隐私安全问题等。通过深入研究和国际合作,未来的材料科学有望通过机器学习技术实现更加智能化和高效的材料设计与发现。
Four kinds of machine learning methods were summarized,including supervised learning,unsupervised learning,deep learning and reinforcement learning.The specific applications of machine learning in material design and discovery,material characterization and computational materials science were discussed,and its potential in accelerating material development and optimization was demonstrated.The database and data mining technology in materials science was introduced,and the development of database and the application of data mining was summarized.The application of emerging large model technologies in material science was summarized,and it was pointed out that the development of large model technologies led material science into a new era of intelligence.However,the current field still faces many challenges,such as data quality,model interpretability and privacy and security concerns.Through in-depth research and international cooperation,the future material science is expected to achieve more intelligent and efficient material design and discovery through machine learning technology.
作者
刘城城
魏海霞
付奎源
苏航
LIU Chengcheng;WEI Haixia;FU Kuiyuan;SU Hang(Institute of Structural Steel,Central Iron and Steel Research Institute,Beijing 100081,China;Material Digital R&D Center,China Iron and Steel Research Institute Group,Beijing 100081,China;Hongxing Iron&Steel Co.,Ltd.,Jiuquan Iron and Steel Group Corporation,Jiayuguan 735100,Gansu,China)
出处
《鞍钢技术》
CAS
2024年第6期34-49,共16页
Angang Technology
基金
国家重点研发计划项目(2021YFB3501502,2021YFB3702500)
中国石油化工股份有限公司项目(323089)。
关键词
机器学习
材料科学
材料设计
数据库
machine learning
material science
material design
database
