Fundamental Microscopic Properties as Predictors of Large-Scale Quantities of Interest: Validation through Grain Boundary Energy Trends

TL;DR

This paper demonstrates that correlations between microscopic properties from approximate interatomic potentials and large-scale quantities like grain boundary energy can be used to predict complex material behaviors, validated against DFT data.

Contribution

It introduces a regression approach linking microscopic properties to large-scale quantities, validated with DFT data, enabling predictive materials modeling beyond first-principles calculations.

Findings

Regression models recover known correlations.

New correlations between microscopic properties and grain boundary energy.

Validation of interatomic potentials against DFT data.

Abstract

Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs) provide an avenue to predictive materials discovery. We propose that such correlations can be efficiently discovered through simulations utilizing approximate interatomic potentials (IPs), which serve as an ensemble of "synthetic materials." As a proof of principle we build a regression model relating canonical properties to the symmetric tilt grain boundary (GB) energy curves in face-centered cubic crystals, characterized by the scaling factor in the universal lattice matching model of Runnels et al. (2016), which we take to be our QoI. Our analysis recovers known correlations of GB energy to other properties and discovers new ones. We also demonstrate, using available density functional theory (DFT) GB energy data, that the regression model constructed from IP data is consistent with DFT results, confirming the assumption that the IPs and DFT belong to same statistical pool and thereby validating the approach. Regression models constructed in this fashion can be used to predict large-scale QoIs based on first-principles data and provide a general method for training IPs for QoIs beyond the scope of first-principles calculations.