通过机器学习和表面研究识别的IrO2表面复杂性
IrO2 Surface Complexions Identified Through Machine Learning and Surface Investigations
摘要：
利用密度泛函理论数据训练高斯近似势（Gaussian Approximation Potential，GAP），通过模拟退火实现低折射率金红石IrO 2晶面的全局几何优化。从头算热力学确定了（101）和（111）（1x 1）-终止在还原环境中与（110）竞争。单晶实验发现（101）晶面占主导地位，并表现出理论预测的（1x 1）周期性和X射线光电子能谱学（XPS）芯能级位移。所获得的结构类似于陶瓷电池材料中讨论的络合物。
英文摘要：
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.
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