MLMD:a programming-free AI platform to predict and design materials  

作　　者：Jiaxuan Ma Bin Cao Shuya Dong Yuan Tian Menghuan Wang Jie Xiong Sheng Sun 

机构地区：[1]Materials Genome Institute,Shanghai University,Shanghai 200444,China [2]Zhejiang Laboratory,Hangzhou 311100,China [3]Guangzhou Municipal Key Laboratory of Materials Informatics,Advanced Materials Thrust,Hong Kong University of Science and Technology(Guangzhou),Guangzhou 510000,China [4]Shanghai Frontier Science Center of Mechanoinformatics,Shanghai University,Shanghai 200444,China

出　　处：《npj Computational Materials》2024年第1期2636-2647,共12页计算材料学(英文)

基　　金：supported by the National Key Research and Development Program of China(Grant No.2022YFB3707803);the National Natural Science Foundation of China(Grants Nos.12072179 and 11672168);the Key Research Project of Zhejiang Lab(Grant No.2021PE0AC02);Shanghai Pujiang Program(Grant No.23PJ1403500);Shanghai Engineering Research Center for Integrated Circuits and Advanced Display Materials.

摘　　要：Accelerating the discovery of advanced materials is crucial for modern industries,aerospace,biomedicine,and energy.Nevertheless,only a small fraction of materials are currently under experimental investigation within the vast chemical space.Materials scientists are plagued by timeconsuming and labor-intensive experiments due to lacking efficient material discovery strategies.Artificial intelligence(AI)has emerged as a promising instrument to bridge this gap.Although numerous AI toolkits or platforms for material science have been developed,they suffer from many shortcomings.These include primarily focusing on material property prediction and being unfriendly to material scientists lacking programming experience,especially performing poorly with limited data.Here,we developed MLMD,an AI platformfor materials design.It is capable of effectively discovering novel materials with high-potential advanced properties end-to-end,utilizing model inference,surrogate optimization,and even working in situations of data scarcity based on active learning.Additionally,it integrates data analysis,descriptor refactoring,hyper-parameters auto-optimizing,and properties prediction.It also provides a web-based friendly interface without need programming and can be used anywhere,anytime.MLMD is dedicated to the integration of material experiment/computation and design,and accelerate the new material discovery with desired one or multiple properties.It demonstrates the strong power to direct experiments on various materials(perovskites,steel,high-entropy alloy,etc).MLMD will be an essential tool for materials scientists and facilitate the advancement of materials informatics.

关 键 词：ALLOY PROGRAMMING utilizing 

分 类 号：TG1[金属学及工艺—金属学]