PASS: Perturbation augmented space group structure sampling for transferable Fe-O machine learning interatomic potential
arXiv:2607.28000
The paper introduces the PASS (Perturbation Augmented Space group structure Sampling) method to create a diverse first‑principles dataset for training a transferable machine‑learning interatomic potential for iron‑oxygen systems, using the atomic cluster expansion framework and demonstrating its accuracy on bulk, surface, and interface properties as well as large‑scale oxidation simulations.
Abstract
Accurate atomistic modelling of iron (Fe) oxidation requires a reliable interatomic potential, which necessitates an extensive and representative first-principles dataset for training the interatomic potential. However, Fe-oxygen (O) system is known for its structural and magnetic complexity, rendering the generation of high-quality dataset challenging. In this work, we propose the Perturbation Augmented Space group structure Sampling (PASS) method to generate extensive and representative dataset consisting of small-cell structures with less than 10 atoms. We present a systematic approach to developing a first of its kind transferable machine learning interatomic potential (MLIP) for Fe-O system based on the atomic cluster expansion (ACE) framework. We thoroughly validate the accuracy and capability of the ACE MLIP across both pure Fe and Fe-O systems through bulk, surface, and interface properties. We showcase the formation of FeO-like structure in large-scale Fe oxidation simulation using the ACE MLIP. This work demonstrates that the PASS method yields an accurate and transferable MLIP which is capable of capturing the reactive complexity of oxide growth while remaining computationally practical for extended systems.
33 pages, 5 figures