Cross-scale covariance for material property prediction.

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

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.

MATHEMATICAL statistics; STATISTICAL errors; REGRESSION analysis; MOLECULAR dynamics; FORECASTING

NPJ Computational Materials, 2025, Vol 11, Issue 1, p1

2057-3960

Academic Journal

10.1038/s41524-024-01453-w