Xiong Yihuang, Chen Weinan, Guo Wenbo, Wei Hua, Dabo Ismaila
Department of Materials Science and Engineering, and Materials Research Institute, The Pennsylvania State University, University Park, PA, USA.
Phys Chem Chem Phys. 2021 Mar 21;23(11):6880-6887. doi: 10.1039/d0cp05595f. Epub 2021 Mar 16.
Tuning the work functions of materials is of practical interest for maximizing the performance of microelectronic and (photo)electrochemical devices, as the efficiency of these systems depends on the ability to control electronic levels at surfaces and across interfaces. Perovskites are promising compounds to achieve such control. In this work, we examine the work functions of more than 1000 perovskite oxide surfaces (ABO) using data-driven (machine-learning) analysis and identify the factors that determine their magnitude. While the work functions of the BO-terminated surfaces are sensitive to the energy of the hybridized oxygen p bands, the work functions of the AO-terminated surfaces exhibit a much less trivial dependence with respect to the filling of the d bands of the B-site atom and of its electronic affinity. This study shows the utility of interpretable data-driven models in analyzing the work functions of cubic perovskites from a limited number of electronic-structure descriptors.
调节材料的功函数对于最大化微电子和（光）电化学器件的性能具有实际意义，因为这些系统的效率取决于控制表面和界面处电子能级的能力。钙钛矿是实现这种控制的有前途的化合物。在这项工作中，我们使用数据驱动（机器学习）分析研究了1000多个钙钛矿氧化物表面（ABO）的功函数，并确定了决定其大小的因素。虽然BO端接表面的功函数对杂化氧p带的能量敏感，但AO端接表面的功函数对B位原子d带的填充及其电子亲和力的依赖性要小得多。这项研究表明，可解释的数据驱动模型在从有限数量的电子结构描述符分析立方钙钛矿的功函数方面具有实用性。
