摘要
合金材料因其丰富可调的几何结构和电子性质,在催化和材料科学领域得到了广泛的应用.其中合金形成能作为一个重要的物理量,对合金材料的形成和催化活性有重要影响.近年来,随着人工智能和数据库的发展,利用机器学习的方法研究和设计新的材料成为新的研究焦点.基于此,通过人工智能的多任务压缩感知算法,结合AB2合金形成能数据库展开了合金形成能描述符和预测研究.首先建立了相应合金形成能的通用描述符,并展开了特征敏感性分析,揭示出合金材料组分的电子性质和几何性质的影响及其相互依赖关系.研究结果显示,该描述符的预测误差低于8.10 kJ·mol^(-1),具有清晰的物理可阐述性,并预测了大量未知合金材料的形成能.
Because of their rich geometric structure and electronic properties,metal alloys have been widely used in catalysis and materials science.Among them,alloys formation energy has an important influence on the formation and catalytic activity of metal alloys.With the development of artificial intelligence and databases in recent years,machine learning has been used to rationally design new materials.Based on the multi-task compressed sensing algotithm in artificial intelligence,the alloy formation energy descriptor of the AB2 alloy formation energy database was investigated.A universal descriptor of the corresponding alloy formation energy was established,and the sensitivity analysis of features revealed the importance of electronic and geometrical properties of metal alloys.The results show that this descriptor has a prediction error lower than 8.10kJ·mol^(-1) and a better physical interpretation.Finally,the formation energy of a large number of unknown metal alloys was predicted.
作者
李健聪
王泰然
舒武
胡素磊
欧阳润海
李微雪
LI Jiancong;WANG Tairan;SHU Wu;HU Sulei;OUYANG Runhai;LI Weixue(Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China;Department of Chemical Physics, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China;Materials Genome Institute, Shanghai University, Shanghai 200444, China)
基金
科技部重点研发计划(2018YFA0208603)
中国科学院前沿重点项目(QYZDJ-SSW-SLH054)
国家自然科学基金重点项目(91645202,91945302)资助.
关键词
合金形成能
数据库
机器学习
描述符
敏感性分析
alloy formation energy
database
machine learning
descriptor
sensitivity analysis
