A computational workflow integrating a stacked ensemble machine learning(SEML)model and a convolutional neural network(CNN)model with evolutionary algorithms has been developed to identify new compositions of FeNiCrCo...A computational workflow integrating a stacked ensemble machine learning(SEML)model and a convolutional neural network(CNN)model with evolutionary algorithms has been developed to identify new compositions of FeNiCrCoCu MPEAs with high bulk modulus and unstable stacking fault energies.The identified compositions were synthesized and tested for their crystal structures and mechanical properties(hardness and Young’s modulus),resulting in single-phase face-centered cubic(FCC)structures.Additionally,the measured Young’s moduli were in good qualitative agreement with computational predictions.The SHapley Additive exPlanations(SHAP)analysis of the SEML model revealed a relationship between elemental concentration and USFE.Meanwhile,SHAP analysis of the CNN models uncovered correlations between the local clustering of MPEA elements and their mechanical properties.This computational workflow,along with the fundamental insights gained,can be readily expanded and applied to the design of MPEAs with different elemental compositions,as well as to materials beyond MPEAs.展开更多
基金supported by GlycoMIP,a National Science Foundation(NSF)Materials Innovation Platform(MIP)funded through Cooperative Agreement DMR-1933525.This work made use of the synthesis facilities of the NSF-funded MIP,Platform for the Accelerated Realization,Analysis,and Discovery of Interface Materials(PARADIM)under Cooperative Agreement DMR-2039380The authors would like to acknowledge Advanced Research Computing(ARC)at Virginia Tech for computational resources+1 种基金This research also used resources of the National Energy Research Scientific Computing Center(NERSC),a scientific computing facility for the Office of Science in the United States Department of Energy,operated under Contract No.DE-AC02-05CH11231F.W.would like to thank Dr.Dan Luo from Lehigh University for the discussion on convolutional neural networks(CNNs)and their application on sequential data similar to the one in this study.
文摘A computational workflow integrating a stacked ensemble machine learning(SEML)model and a convolutional neural network(CNN)model with evolutionary algorithms has been developed to identify new compositions of FeNiCrCoCu MPEAs with high bulk modulus and unstable stacking fault energies.The identified compositions were synthesized and tested for their crystal structures and mechanical properties(hardness and Young’s modulus),resulting in single-phase face-centered cubic(FCC)structures.Additionally,the measured Young’s moduli were in good qualitative agreement with computational predictions.The SHapley Additive exPlanations(SHAP)analysis of the SEML model revealed a relationship between elemental concentration and USFE.Meanwhile,SHAP analysis of the CNN models uncovered correlations between the local clustering of MPEA elements and their mechanical properties.This computational workflow,along with the fundamental insights gained,can be readily expanded and applied to the design of MPEAs with different elemental compositions,as well as to materials beyond MPEAs.
