Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

作     者：Yonghui Zhou Bo Yang Yonghui Zhou;Bo Yang

作者机构：School of Physical Science and TechnologyShanghaiTech UniversityShanghai 201210China 

出 版 物：《Journal of Energy Chemistry》 (能源化学（英文版）)

年 卷 期：2023年第81卷第6期

页      面：118-124,I0005页

核心收录：

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0714[理学-统计学（可授理学、经济学学位）] 0811[工学-控制科学与工程] 0701[理学-数学] 0702[理学-物理学] 

基　　金：supported by the Shanghai Rising-Star Program (20QA1406800) the National Natural Science Foundation of China (22072091,91745102,92045301)。 

主　　题：Uncertainty quantification High-entropy alloys Bayesian neural networks Energy prediction Structure screening 

摘      要：High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.