Quantifying chemical short-range order in metallic alloys

TL;DR

This paper introduces a multiscale machine learning approach to quantify chemical short-range order in metallic alloys, linking atomic-scale predictions with experimental data and fundamental physical quantities.

Contribution

It presents a novel multiscale method that combines machine learning and electronic-structure calculations to quantify and predict SRO in alloys at atomic resolution.

Findings

Predicted SRO length scales agree with experimental measurements.

Correlated SRO with local lattice distortions.

Enabled quantitative analysis of solid-solution phases.

Abstract

Metallic alloys often form phases - known as solid solutions - in which chemical elements are spread out on the same crystal lattice in an almost random manner. The tendency of certain chemical motifs to be more common than others is known as chemical short-range order (SRO) and it has received substantial consideration in alloys with multiple chemical elements present in large concentrations due to their extreme configurational complexity (e.g., high-entropy alloys). Short-range order renders solid solutions "slightly less random than completely random", which is a physically intuitive picture, but not easily quantifiable due to the sheer number of possible chemical motifs and their subtle spatial distribution on the lattice. Here we present a multiscale method to predict and quantify the SRO state of an alloy with atomic resolution, incorporating machine learning techniques to bridge the gap between electronic-structure calculations and the characteristic length scale of SRO. The result is an approach capable of predicting SRO length scale in agreement with experimental measurements while comprehensively correlating SRO with fundamental quantities such as local lattice distortions. This work advances the quantitative understanding of solid-solution phases, paving the way for SRO rigorous incorporation into predictive mechanical and thermodynamic models.