Physics-Informed Artificial Intelligence for Predicting Operando Electronic State Evolution and Oxygen Evolution Activity in Transition-Metal Oxide Catalysts
Keywords:
physics-informed machine learning, operando spectroscopy, oxygen evolution reaction, transition-metal oxides, electronic structure prediction, sustainable catalysis, AI governanceAbstract
The rational design of transition-metal oxide catalysts for the oxygen evolution reaction demands a predictive understanding of electronic state evolution under operando conditions, a challenge that sits at the intersection of condensed matter physics, data science, and large-scale cyberinfrastructure. Physics-informed artificial intelligence models that fuse mechanistic knowledge with data-driven learning offer a transformative pathway for forecasting dynamic oxidation states, coordination geometries, and catalytic activity directly from multimodal spectroscopic signatures. This paper presents a system-level analysis of the architectures, data ecosystems, and governance frameworks required to deploy such models reliably and at scale. We examine how hybrid neural network designs embed thermodynamic constraints and electronic structure rules to improve extrapolation beyond training distributions, and how federated data pipelines integrating synchrotron operando spectroscopy, high-throughput experimentation, and electronic structure databases can be organized under FAIR principles. The discussion addresses structural trade-offs between model fidelity and computational cost, robustness under distributional shift, uncertainty quantification for safety-critical energy applications, and the governance challenges arising from automated experimentation loops that blur traditional boundaries between human and machine decision-making. We further analyze the sustainability implications of training large models and the policy instruments needed to ensure equitable access to predictive tools and the resulting catalyst innovations. By treating the predictive workflow as a socio-technical system rather than an isolated algorithmic task, the paper identifies leverage points for institutional design, standardization, and responsible innovation that will determine whether physics-informed artificial intelligence can fulfill its promise of accelerating the discovery of active, stable, and earth-abundant oxygen evolution catalysts.
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