Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks

TL;DR

This paper introduces an E(3)-equivariant graph neural network framework for fully characterizing chemical motifs in crystalline materials, enabling detailed analysis of chemical short-range order and structure-property relationships.

Contribution

It presents a novel, complete method for identifying chemical motifs in complex crystalline structures using graph neural networks, surpassing traditional partial characterization techniques.

Findings

Effective identification of chemical motifs in complex structures

Quantification of chemical short-range order using information-theoretic measures

Analysis of temperature-dependent chemical fluctuation length scales

Abstract

Crystalline materials have atomic-scale fluctuations in their chemical composition that modulate various mesoscale properties. Establishing chemistry-microstructure relationships in such materials requires proper characterization of these chemical fluctuations. Yet, current characterization approaches (e.g., Warren-Cowley parameters) make only partial use of the complete chemical and structural information contained in local chemical motifs. Here we introduce a framework based on E(3)-equivariant graph neural networks that is capable of completely identifying chemical motifs in arbitrary crystalline structures with any number of chemical elements. This approach naturally leads to a proper information-theoretic measure for quantifying chemical short-range order (SRO) in chemically complex materials, and a reduced - but complete - representation of the chemical space. Our framework enables the correlation of any per-atom property with their corresponding local chemical motif, thereby offering novel avenues to explore structure-property relationships in chemically-complex materials. Using the MoTaNbTi high-entropy alloy as a test system, we demonstrate the versatility of this approach by evaluating the lattice strain associated with each chemical motif, and computing the temperature dependence of chemical-fluctuations length scale.