An approach for full space inverse materials design by combining universal machine learning potential,universal property model,and optimization algorithm  

结合通用机器学习势和优化算法的全空间逆向材料设计方法

作　　者：Guanjian Cheng Xin-Gao Gong Wan-Jian Yin 程观剑;龚新高;尹万健

机构地区：[1]College of Energy,Soochow Institute for Energy and Materials InnovationS(SIEMIS),and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies,Soochow University,Suzhou 215006,China [2]Shanghai Qi Zhi Institute,Shanghai 200232,China [3]Key Laboratory for Computational Physical Sciences(MOE),Institute of Computational Physical Sciences,Fudan University,Shanghai 200438,China

出　　处：《Science Bulletin》2024年第19期3066-3074,共9页科学通报（英文版）

基　　金：funding support by the National Key Research and Development Program of China(2020YFB1506400);the National Natural Science Foundation of China(11974257 and 12188101);Jiangsu Distinguished Young Talent Funding(BK20200003);Soochow Municipal Laboratory for low carbon technologies and industries.

摘　　要：We present a full space inverse materials design(FSIMD)approach that fully automates the materials design for target physical properties without the need to provide the atomic composition,chemical stoichiometry,and crystal structure in advance.Here,we used density functional theory reference data to train a universal machine learning potential(UPot)and transfer learning to train a universal bulk modulus model(UBmod).Both UPot and UBmod were able to cover materials systems composed of any element among 42 elements.Interfaced with optimization algorithm and enhanced sampling,the FSIMD approach is applied to find the materials with the largest cohesive energy and the largest bulk modulus,respectively.NaCl-type ZrC was found to be the material with the largest cohesive energy.For bulk modulus,diamond was identified to have the largest value.The FSIMD approach is also applied to design materials with other multi-objective properties with accuracy limited principally by the amount,reliability,and diversity of the training data.The FSIMD approach provides a new way for inverse materials design with other functional properties for practical applications.

关 键 词：Inverse materials design Universal machine learning potential Graph neural networks Bayesian optimization 

分 类 号：TB30[一般工业技术—材料科学与工程] TP181[自动化与计算机技术—控制理论与控制工程]