Machine Learning-Guided Multimodal Synchrotron Analysis Workflow for Fuel Cell Electrocatalyst Discovery.

Synchrotron radiation provides exceptional sensitivity and resolution, enabling the acquisition of highly precise information critical for advancing fuel cell technology. When combined with machine learning-based, data-driven approaches, it offers powerful insights into reaction pathways and is poised to significantly accelerate the discovery of next-generation fuel cell catalysts. However, the singular characterization and complex feature space of synchrotron radiation data, necessitates a novel approach to obtain structural insights into the fuel cell catalyst. In this work, we propose a novel framework for rational electrocatalyst discovery that integrates machine learning with multimodal spectral descriptors derived from advanced synchrotron radiation techniques-XANES, EXAFS, XRD, SAXS, PDF, and HAXPES (Pt3d, Pt4f, and VB). We employed structure-performance prediction machine learning model to identify key multimodality descriptors. By assessing the importance of these modalities, we established a reverse-engineering framework for catalyst discovery, enabling the structural inference of new high-performance catalyst candidates. The experimentally derived descriptor space was validated through physics-based theoretical modelling, effectively narrowing the pool of potential candidates, and enabling the precise identification of the optimal structure-performance electrocatalyst. The proposed framework enables a shift, beyond empirical catalyst screening, toward a more efficient, interpretable, and high-throughput strategy for the discovery and design of next-generation electrocatalysts.