Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals  

作　　者：A.Mahata T.Mukhopadhyay S.Chakraborty M.Asle Zaeem 

机构地区：[1]Department of Engineering,King’s College,Wilkes-Barre,PA 18711,USA [2]School of Engineering,Brown University,Providence,RI 02912,USA [3]Faculty of Engineering and Physical Sciences,University of Southampton,Southampton,UK [4]Department of Applied Mechanics,Indian Institute of Technology Delhi,Delhi,India [5]Department of Mechanical Engineering,Colorado School of Mines,Golden,CO 80401,USA

出　　处：《npj Computational Materials》2024年第1期3024-3035,共12页计算材料学(英文)

基　　金：supported by the National Science Foundation,CMMI 2031800;The authors are grateful for the supercomputing time allocation provided by the NSF’s ACCESS(Advanced Cyberinfrastructure Coordination Ecosystem:Services&Support),Award No.DMR140008 and MAT210018.

摘　　要：Solidification phenomenon has been an integral part of the manufacturing processes of metals,where the quantification of stochastic variations and manufacturing uncertainties is critically important.Accurate molecular dynamics(MD)simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary.The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization,causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo(MC)simulations.In this work,we present a machine learning(ML)approach,as a data-driven surrogate to MD simulations,which only needs a few MD simulations.This efficient yet high-fidelity ML approach enables MC simulations for fullscale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate.Unlike conventional ML models,the proposed hybrid polynomial correlated function expansion here,being a Bayesian ML approach,is data efficient.Further,it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations,which in principle addresses the issue of repeatability in stochastic simulations with low variance.Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus,yield strength and ultimate strength,illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency.

关 键 词：SOLIDIFICATION PROBABILISTIC ERROR 

分 类 号：TG1[金属学及工艺—金属学]