Cross-scale covariance for material property prediction
Abstract
A simulation can stand its ground against 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 ( atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales ( 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 quantify uncertainty 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 uncertainty bounds established in our statistical study.
Cite
@article{arxiv.2406.05146,
title = {Cross-scale covariance for material property prediction},
author = {Benjamin A. Jasperson and Ilia Nikiforov and Amit Samanta and Fei Zhou and Ellad B. Tadmor and Vincenzo Lordi and Vasily V. Bulatov},
journal= {arXiv preprint arXiv:2406.05146},
year = {2025}
}