IrO2 Surface Complexions Identified through Machine Learning and Surface Investigations
IrO2 Surface Complexions Identified through Machine Learning and Surface Investigations
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通过机器学习和表面研究识别 IrO2 表面肤色
DOI：
10.1103/physrevlett.125.206101
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发表时间：
2020
影响因子：
8.6
通讯作者：
Schmid, Michael
中科院分区：
文献类型：
--
作者：
Timmermann, Jakob;Kraushofer, Florian;Resch, Nikolaus;Li, Peigang;Wang, Yu;Mao, Zhiqiang;Riva, Michele;Lee, Yonghyuk;Staacke, Carsten;Schmid, Michael
利用密度泛函理论数据训练高斯近似势，通过模拟退火对低折射率金红石晶面进行全局几何优化，用从头算热力学方法确定了（101）和（111）（）在还原环境中与（110）竞争的终止方式.单晶实验发现，（101）晶面占主导地位，并表现出理论预测的（）周期性和x射线光电子能谱芯能级位移。所获得的结构类似于陶瓷电池材料的上下文中讨论的络合物。
A Gaussian approximation potential was trained using density-functional theory data to enable a global geometry optimization of low-index rutilefacets through simulated annealing.Ab initiothermodynamics identifies (101) and (111) () terminations competitive with (110) in reducing environments. Experiments on single crystals find that (101) facets dominate and exhibit the theoretically predicted () periodicity and x-ray photoelectron spectroscopy core-level shifts. The obtained structures are analogous to the complexions discussed in the context of ceramic battery materials.