Tukur Panesun, Wei Yong, Zhang Yinning, Chen Hanning, Lin Yuewei, He Selena, Mo Yirong, Wei Jianjun
Department of Nanoscience, The Joint School of Nanoscience and Nanoengineering, University of North Carolina at Greensboro, 2907 E. Gate City Blvd, Greensboro, NC, 27401, USA.
Department of Computer Science and Information Systems, University of North Georgia, 82 College Cir, Dahlonega, GA, 30597, USA.
Small. 2025 May;21(21):e2501946. doi: 10.1002/smll.202501946. Epub 2025 Apr 8.
High-entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs' structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure-property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real-world applications. This approach accelerates the discovery of high-performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.
高熵钙钛矿氧化物（HEPOs）最近已成为多功能催化剂。然而，HEPOs的结构和组成复杂性阻碍了对活性指标的轻松准确推断，而活性指标对于建立结构-性能相关性至关重要。在此，引入了OxiGraphX，这是一种新颖的图神经网络（GNN）模型，旨在捕捉结构、组成和原子化学环境之间的复杂关系，以准确预测HEPOs中的氧空位形成能（OVFEs）。通过整合机器学习（ML）、密度泛函理论（DFT）和实验验证，这项工作展示了一个用于快速准确筛选用于析氧反应（OER）的HEPO电催化剂的有效框架。OxiGraphX预测OVFEs的精度超过现有数据，能够识别具有更高氧空位含量（OVC）的组成，从而具有更高的催化活性。此外，该模型探索了能够有效转化为实验领域的潜在空间，将计算预测与实际应用联系起来。这种方法加速了高性能HEPO催化剂的发现，同时提供了对其催化机制更深入的见解。
