Explainable Machine Learning Framework for Decoding Zhang–Rice Singlet Formation and Oxygen Evolution Reaction Kinetics
Keywords:
explainable machine learning, Zhang–Rice singlet, oxygen evolution reaction, operando spectroscopy, catalyst informatics, model interpretability, data infrastructure, governanceAbstract
Deciphering the electronic structure origin of catalytic activity in the oxygen evolution reaction (OER) remains a central challenge for sustainable energy technologies. The Zhang–Rice singlet, a two-hole bound state on a single transition metal site, has emerged as a key descriptor linking local electronic structure to OER kinetics, yet its formation pathways under operando conditions are highly complex. This paper presents a system-level explainable machine learning framework that decodes Zhang–Rice singlet formation and OER kinetics from multimodal operando spectroscopy data. The architecture couples deep representation learning with post-hoc interpretability techniques, enabling the extraction of physically meaningful features while preserving high predictive fidelity. We examine structural trade-offs between model accuracy, transparency, and computational cost across a distributed data infrastructure built on cloud-native, FAIR-compliant pipelines. The analysis extends to governance and fairness concerns, including dataset bias, underrepresentation of catalyst families, and the need for federated learning to protect intellectual property while enabling collaborative model refinement. Robustness is addressed through uncertainty quantification and adversarial validation protocols, while sustainability considerations motivate efficiency-aware model selection and resource budgeting. Policy implications concerning open science mandates, reproducibility, and equitable access to advanced catalyst informatics tools are discussed. By integrating technical and socio-technical dimensions, the proposed framework establishes a principled pathway toward interpretable, responsible, and scalable data-driven discovery in electrocatalysis.
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