Title: Modelling Surface Segregation in Compositionally Complex Alloys with Ab-Initio Accuracy Show Chinese title

Title: 用第一性原理精度建模成分复杂合金的表面偏析

Abstract: Compositionally complex alloys or concentrated solid solutions are the latest frontier in catalyst design, but mixing different elements in one catalyst may result in surface segregation. Atomistic simulations can predict segregation patterns, but standard approaches based on mean-field models, cluster expansion, or classical interatomic potentials are often limited for the description of multicomponent alloys. We present machine learning potentials that can describe surface segregation with near DFT accuracy. The method is used to study a complex Co-Cu-Fe-Mo-Ni quinary alloy. For this alloy, an unexpected segregation of Co, which has a relatively high surface energy, is observed. We rationalize this surprising mechanism in terms of simple transition-metal chemistry. Show Chinese abstract

Abstract: 组合复杂的合金或高浓度固溶体是催化剂设计的最新前沿，但在一个催化剂中混合不同元素可能会导致表面偏析。原子模拟可以预测偏析模式，但基于平均场模型、团簇展开或经典原子间势的标准方法在描述多组分合金时通常受到限制。我们提出了机器学习势能，可以以接近密度泛函理论（DFT）的精度描述表面偏析。该方法用于研究一种复杂的钴-铜-铁-钼-镍五元合金。对于这种合金，观察到钴的意外偏析，钴具有相对较高的表面能。我们从简单的过渡金属化学角度解释了这一令人惊讶的机制。