Short-Range Order Based Ultra Fast Large-Scale Modeling of High-Entropy Alloys

TL;DR

PyHEA is a high-performance Python toolkit that significantly accelerates large-scale modeling of High-Entropy Alloys by leveraging GPU computing and advanced algorithms, enabling realistic simulations at unprecedented speeds.

Contribution

The paper introduces PyHEA, a novel Python-based toolkit with a C++ core that drastically improves the speed and efficiency of modeling HEAs, surpassing traditional methods by over 100,000 times.

Findings

PyHEA achieves speedups over 133,000x compared to ATAT-mcsqs.

PyHEA can model a 256,000-atom system within minutes.

The toolkit maintains high accuracy while enabling large-scale simulations.

Abstract

High-Entropy Alloys (HEAs) exhibit complex atomic interactions, with short-range order (SRO) playing a critical role in determining their properties. Traditional methods, such as Monte Carlo generator of Special Quasirandom Structures within the Alloy Theoretic Automated Toolkit (ATAT-mcsqs), Super-cell Random Approximates (SCRAPs), and hybrid Monte Carlo-Molecular Dynamics (MC-MD) are often hindered by limited system sizes and high computational costs. In response, we introduce PyHEA, a Python-based toolkit with a high-performance C++ core that leverages global and local search algorithms, incremental SRO computations, and GPU acceleration for unprecedented efficiency. When constructing random HEAs, PyHEA achieves speedups exceeding 133,000x and 13,900x over ATAT-mcsqs and SCRAPs, respectively, while maintaining high accuracy. PyHEA also offers a flexible workflow that allows users to incorporate target SRO values from external simulations (e.g., LAMMPS or density functional theory (DFT)), thereby enabling more realistic and customizable HEA models. As a proof of concept, PyHEA successfully replicated literature results for a 256,000-atom Fe-Cr-Co system within minutes-an order-of-magnitude improvement over hybrid MC-MD approaches. This dramatic acceleration opens new possibilities for bridging theoretical insights and practical applications, paving the way for the efficient design of next-generation HEAs.