Cross-scale covariance for material property prediction

Jasperson, Benjamin A.; Nikiforov, Ilia; Samanta, Amit; Zhou, Fei; Tadmor, Ellad B.; Lordi, Vincenzo; Bulatov, Vasily V.

doi:10.1038/s41524-024-01453-w

Title: Cross-scale covariance for material property prediction

Journal Article · 03 January 2025 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-024-01453-w · OSTI ID:2522858

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Univ. of Illinois at Urbana-Champaign, IL (United States)
Univ. of Minnesota, Minneapolis, MN (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

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 (~10⁸ atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤10² atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze 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.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 2522858

Report Number(s):: LLNL--JRNL-860766; 1092479

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 11; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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