Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce optimal designs, particularly through the calculation of descriptors for activity. In this study, we develop a dataset of density functional theory calculations of bulk and surface perovskite oxides, and adsorption energies of OER intermediates, which includes compositions up to quaternary and facets up to (555). We demonstrate that per-site properties of perovskite oxides such as Bader charge or band center can be tuned through element substitution and faceting, and develop a machine learning model that accurately predicts these properties directly from the local chemical environment. We leverage these per-site properties to identify promising perovskites with high theoretical OER activity. The identified design principles and promising materials provide a roadmap for closing the gap between current artificial catalysts and biological enzymes such as photosystem II.

绿色氢气生产对于可持续的未来至关重要，但目前的析氧反应（OER）催化剂仍存在动力学缓慢的问题，尽管人们在优化设计方面做出了许多努力，特别是通过计算活性描述符。在这项研究中，我们开发了体积和表面钙钛矿氧化物的密度泛函理论计算数据集，以及 OER 中间体的吸附能，其中包括高达四元的成分和高达 (555) 的晶面。我们证明了钙钛矿氧化物的每个位点特性（例如巴德电荷或能带中心）可以通过元素替换和刻面来调整，并开发了一种机器学习模型，可以直接从局部化学环境准确预测这些特性。我们利用这些每个位点的特性来识别具有高理论 OER 活性的有前景的钙钛矿。确定的设计原理和有前景的材料为缩小当前人工催化剂和光系统 II 等生物酶之间的差距提供了路线图。