Cross-scale covariance for material property prediction

TL;DR

This paper investigates how small-scale properties correlate with large-scale material strength predictions using molecular dynamics simulations, enabling uncertainty quantification and improved prediction accuracy.

Contribution

It introduces a cross-scale covariance analysis linking small-scale indicators to large-scale strength, and develops a regression model to quantify uncertainty in material property predictions.

Findings

Strong covariance between small-scale indicators and strength identified

Quantum-accurate small-scale calculations improve large-scale strength predictions

Uncertainty bounds for predictions are established based on statistical analysis

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 ($\sim 10^8$ atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales ($\leq 10^2$ 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.