Physics-Guided Graph Neural Networks for Interpretable Active-Site Evolution Prediction in Water Oxidation Catalysts

Abstract

Water oxidation is a cornerstone reaction for renewable energy storage and sustainable hydrogen production, yet the rational design of efficient and durable catalysts remains constrained by an incomplete understanding of how active sites dynamically restructure under operating conditions. Physics-guided graph neural networks offer a transformative framework to bridge this gap, integrating first-principles physical constraints with data-driven learning to predict the structural and electronic evolution of catalytic surfaces. This paper presents a systems-level examination of such a hybrid modeling paradigm, focusing on the architecture, interpretability mechanisms, data infrastructure, and deployment strategies that underpin reliable active-site evolution predictions. We discuss how graph neural networks encode atomistic environments as graph structures whose nodes and edges capture coordination chemistry and electronic interactions, while physics-guided regularizations enforce thermodynamic and spectroscopic consistency. Special attention is devoted to interpretability, enabling researchers to trace which structural motifs and electronic signatures drive activity, including operando spectroscopic fingerprints such as the formation of Zhang-Rice singlet states. The discussion extends to data governance, emphasizing the need for FAIR-compliant data ecosystems that amalgamate high-throughput experiments, density functional theory calculations, and real-time spectroscopy. Robustness against distributional shifts, fairness in catalyst discovery across material families, and the sustainability implications of AI-accelerated catalyst pipelines are addressed as cross-cutting concerns. Finally, policy considerations surrounding open science, intellectual property, and equitable access to AI tools for the global energy transition are explored. The paper argues that a tightly coupled socio-technical infrastructure, where domain-informed model architecture and transparent governance coexist, is essential for translating physics-guided graph neural networks from laboratory proof-of-concept to impactful, field-deployable decision-support systems.