Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions

TL;DR

This paper introduces a novel data generation protocol and a robust machine learning model to accurately simulate complex multi-elemental materials under extreme conditions, overcoming traditional dataset biases.

Contribution

It presents a chemistry-agnostic, entropy-maximization data generation method and a Graph Atomic Cluster Expansion model that improve robustness in modeling complex, multi-element materials.

Findings

Enhanced robustness in simulations of phase transformations and defect evolution.

Improved accuracy in catalytic reaction barrier predictions.

Scalable approach for unbiased exploration of multi-elemental materials.

Abstract

Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an emerging circular economy, together with the need to understand materials behavior under planetary and industrial extremes, increasingly require mastering Mendeleev materials - chemically and structurally complex systems that span large portions of the periodic table. In these regimes, current universal machine-learning interatomic potentials often fail, largely due to systematic gaps in traditional training datasets that heavily emphasize low-energy, near-equilibrium structures. We address this limitation by introducing a chemistry-agnostic, information-entropy-maximization protocol for data generation. By decoupling structural sampling from thermodynamic bias, our approach provides a robust physical prior for atomic interactions across the entire periodic table, including regimes far from equilibrium and under extreme conditions. Training a Graph Atomic Cluster Expansion (GRACE) model on the resulting statistically maximized entropy (SMAX) dataset yields markedly improved robustness across a range of stringent benchmarks. These include large-strain phase transformations in tin, defect evolution in tungsten-based alloys, and catalytic reaction barrier prediction. More broadly, our approach establishes a scalable and principled methodology for navigating the vast chemical and configurational space relevant to future materials design. It enables a paradigm of discovery by simulation in which unbiased sampling protocols autonomously resolve emergent structures in multi-elemental mixtures-such as systems containing the nine most abundant elements in the Earth's crust-without reliance on a priori chemical assumptions.