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Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling
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DOI：10.1016/j.commatsci.2026.114752.png)
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Abstract
En 中文
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.
Keywords:
High-entropy alloys
Interstitial defects
Machine-learning interatomic potentials
Monte Carlo sampling
Atomic-scale structure
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3.3
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1.3W
Citations:
3.6W
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