Ground-State Structure Search of Defective High-Entropy Alloys Using Machine-Learning Potentials and Monte Carlo Sampling

TL;DR

This paper introduces PAIPAI, a machine-learning and Monte Carlo-based framework that efficiently predicts the atomic structure of defective high-entropy alloys, outperforming random sampling and validated by DFT calculations.

Contribution

The paper presents a novel parallel Monte Carlo framework combined with machine-learning potentials for accurate ground-state structure prediction in defective high-entropy alloys.

Findings

Monte Carlo-optimized structures are lower in energy than random sampling.

MLIP energy rankings are validated against DFT calculations.

PAIPAI effectively predicts atomic ordering and segregation in complex HEAs.

Abstract

Resolving the atomic-scale structure of defective high-entropy alloys (HEAs) containing interstitial species remains a major computational challenge due to the vast configurational space and the limitations of existing methods. Here we introduce PAIPAI (Package for Alloy Interstitial Predictions using Artificial Intelligence), a Monte Carlo framework coupled with machine-learning interatomic potentials (MLIPs) that searches for ground-state atomic configurations in HEAs with defects and interstitials. PAIPAI employs a dual-worker architecture-fast workers for rapid configurational screening and slow workers for high-accuracy refinement-coordinated through a shared waiting pool, enabling efficient parallel sampling. We demonstrate PAIPAI through three case studies: (i) surface segregation in a Ti-V-Cr-Re slab; (ii) interstitial oxygen and boron aggregation in bulk BCC Nb-Ti-Ta-Hf; and (iii) coupled metallic and interstitial segregation at grain boundaries in Nb-Ti-Ta-Hf. In all cases, Monte Carlo-optimized structures are significantly lower in energy than any configuration obtained by random sampling, and MLIP energy rankings are validated against density functional theory calculations. PAIPAI provides a general and efficient framework for predicting atomic ordering, segregation, and interstitial behavior in complex, defective HEA systems.