AI 中文总结
本研究提出PASS方法生成Fe-O体系小晶胞数据集，基于ACE框架开发可迁移MLIP，经多性质验证，可用于大规模Fe氧化模拟。
AI 中文摘要
铁（Fe）氧化的精确原子建模需要可靠的原子间势，这就需要用于训练原子间势的广泛且具有代表性的第一性原理数据集。然而，铁-氧（Fe-O）体系以结构和磁复杂性著称，使得高质量数据集的生成颇具挑战。本研究提出了微扰增强空间群结构采样（PASS）方法，用于生成由含少于10个原子的小晶胞结构组成的广泛且具有代表性的数据集。我们基于原子簇展开（ACE）框架，开发了首个用于Fe-O体系的可迁移机器学习原子间势（MLIP）。我们通过块体、表面和界面性质，全面验证了ACE MLIP在纯Fe和Fe-O体系中的准确性与能力。我们展示了使用该ACE MLIP进行大规模Fe氧化模拟时FeO类结构的形成。本研究表明，PASS方法生成的准确且可迁移的MLIP能够捕获氧化物生长的反应复杂性，同时对于扩展体系仍具备计算可行性。
英文摘要
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.
Comments33 pages, 5 figures
