Bayesian blacksmithing:discovering thermomechanical properties and deformation mechanisms in high-entropy refractory alloys  

作　　者：Jacob Startt Megan J.McCarthy Mitchell A.Wood Sean Donegan Rémi Dingreville 

机构地区：[1]Center for Integrated Nanotechnologies,Sandia National Laboratories,Albuquerque,NM,87185,USA [2]Center for Computing Research,Computational Multiscale Department,Sandia National Laboratories,Albuquerque,NM,87185,USA [3]Manufacturing and Industrial Technologies Division,Air Force Research Laboratory,Wright Patterson AFB,OH,45433,USA

出　　处：《npj Computational Materials》2024年第1期1545-1557,共13页计算材料学(英文)

摘　　要：Finding alloys with specific design properties is challenging due to the large number of possible compositions and the complex interactions between elements.This study introduces a multiobjective Bayesian optimization approach guiding molecular dynamics simulations for discovering high-performance refractory alloys with both targeted intrinsic static thermomechanical properties and also deformation mechanisms occurring during dynamic loading.The objective functions are aiming for excellent thermomechanical stability via a high bulk modulus,a low thermal expansion,a high heat capacity,and for a resilient deformation mechanism maximizing the retention of the BCC phase after shock loading.Contrasting two optimization procedures,we show that the Pareto-optimal solutions are confined to a small performance space when the property objectives display a cooperative relationship.Conversely,the Pareto front is much broader in the performance space when these properties have antagonistic relationships.Density functional theory simulations validate these findings and unveil underlying atomic-bond changes driving property improvements.

关 键 词：deformation ALLOYS THERMOMECHANICAL 

分 类 号：TG1[金属学及工艺—金属学]