A Theoretical Framework for the Most Probable Distribution of Meta-structures in Materials

TL;DR

This paper develops a theoretical framework based on Boltzmann's principle to predict the most probable distribution of meta-structures in materials, validated through simulations and applied to alloy design.

Contribution

It introduces a novel theoretical approach for predicting meta-structure distributions, integrating statistical mechanics, high-throughput computations, and machine learning.

Findings

Validated the framework with binary alloy simulations

Determined atomic chemical potentials in FeCr alloy

Paved the way for atomic-scale material design

Abstract

Inspired by the principle of equal probability proposed by Boltzmann in the 1870s, we establish a theoretical framework for the most probable distribution of meta-structures in materials. Furthermore, we validate the reliability of this theoretical framework based on statistical results of these meta-structures within randomly generated binary alloys. Finally, combining this theoretical framework, the high-throughput first-principles computations and machine learning, we determine the atomic chemical potentials in the binary FeCr alloy, thereby providing a demonstrative application. This theoretical framework will open a new research area and lay a foundation for the atomic-scale design of targeted properties.