A simulation can stand its ground against an experiment only if its prediction uncertainty is known.The unknown accuracy of interatomic potentials(IPs)is a major source of prediction uncertainty,severely limiting the ...A simulation can stand its ground against an experiment only if its prediction uncertainty is known.The unknown accuracy of interatomic potentials(IPs)is a major source of prediction uncertainty,severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications.Here we explore covariance between predictions of metal plasticity,from 178 large-scale(~10^(8)atoms)molecular dynamics(MD)simulations,and a variety of indicator properties computed at small-scales(≤10^(2)atoms).All simulations use the same 178 IPs.In a manner similar to statistical studies in public health,weanalyze correlations of strength with indicators,identify the best predictor properties,and build a cross-scale“strength-on-predictors”regression model.This model is then used to estimate regression error over the statistical pool of IPs.Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength,within the statistical error bounds established in our study.展开更多
基金supported by the National Science Foundation(NSF)under grant no.1922758.E.B.T.and I.N.acknowledge partial support through NSF under grant no.1834251 and 1834332funding support from the Laboratory Directed Research and Development program(tracking number 23-SI-006)+1 种基金a special computational time allocation on the Lassen supercomputer from the Computational Grand Challenge program at Lawrence Livermore National LaboratoryThis work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
文摘A simulation can stand its ground against an experiment only if its prediction uncertainty is known.The unknown accuracy of interatomic potentials(IPs)is a major source of prediction uncertainty,severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications.Here we explore covariance between predictions of metal plasticity,from 178 large-scale(~10^(8)atoms)molecular dynamics(MD)simulations,and a variety of indicator properties computed at small-scales(≤10^(2)atoms).All simulations use the same 178 IPs.In a manner similar to statistical studies in public health,weanalyze correlations of strength with indicators,identify the best predictor properties,and build a cross-scale“strength-on-predictors”regression model.This model is then used to estimate regression error over the statistical pool of IPs.Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength,within the statistical error bounds established in our study.
