论文标题
通过机器学习和表面研究确定的IRO2表面肤色
IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations
论文作者
论文摘要
使用密度功能理论数据对高斯近似电势（GAP）进行训练，以通过模拟退火来实现低索引Rutile IRO2方面的全球几何形状优化。从头开始，热力学识别（101）和（111）（1x1） - 在还原环境中竞争（110）。对单晶的实验发现（101）方面占主导地位，并表现出理论上预测的（1x1）周期性和X射线光电光谱（XPS）核心水平移动。所获得的结构类似于陶瓷电池材料中讨论的肤色。
A Gaussian Approximation Potential (GAP) was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO2 facets through simulated annealing. Ab initio thermodynamics identifies (101) and (111) (1x1)-terminations competitive with (110) in reducing environments. Experiments on single crystals find that (101) facets dominate, and exhibit the theoretically predicted (1x1) periodicity and X-ray photoelectron spectroscopy (XPS) core level shifts. The obtained structures are analogous to the complexions discussed in the context of ceramic battery materials.
