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

ABSTRACT: 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

SUBMITTER: Baliyan A 

PROVIDER: S-EPMC12660924 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature