论文标题
具有Ab-Initio精度的成分复杂合金中的建模表面分离
Modelling Surface Segregation in Compositionally Complex Alloys with Ab-Initio Accuracy
论文作者
论文摘要
成分复杂的合金或浓缩固体溶液是催化剂设计中最新的边界，但是在一种催化剂中混合不同的元素可能会导致表面隔离。原子模拟可以预测隔离模式，但是基于平均场模型，群集扩展或经典原子势的标准方法通常受到限制以描述多组分合金的描述。我们提出机器学习潜力，可以用几乎DFT的精度来描述表面隔离。该方法用于研究复杂的Co-Cu-Fe-Mo-Ni Quinary合金。对于这种合金，观察到表面能相对较高的CO的意外隔离。我们从简单的过渡金属化学方面合理化了这种令人惊讶的机制。
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.
