General workflow for localizing hydrides in metal nanoclusters by combining stochastic surface walking with neural-network potentials

Ligand-protected metal hydride nanoclusters are crucial for applications in catalysis, luminescence, and energy technologies. However, accurately locating hydrogen atoms (hydrides) within these complex structures remains a significant challenge, hindering the full exploitation of their properties. Developing a universal and accessible method for hydride localization is essential. Here, we present a general computational workflow that combines global structural search algorithms with machine learning-based neural-network potentials to efficiently locate hydrides. We validate this approach across 93 experimentally reported systems, including coinage-metal, transition-metal, and multimetallic polyoxometalates. Using this framework, here we show the generalized rules governing hydride positioning and their preferred coordination environments. Furthermore, we reveal the atomic-level dynamics of hydride movement, discovering that surface migration is the predominant pathway. Practically, our approach provides a reliable theoretical supplement to resolve uncertainties in experimental hydride quantification, such as those from mass spectrometry. Overall, this study advances the fundamental understanding of hydride behavior in nanoclusters and offers a robust, predictive tool to guide the synthesis and structural characterization of nanomaterials. Accurately determining the positions of hydrides in metal nanoclusters is challenging. Here, the authors present a machine-learning-based workflow that efficiently identifies hydrides and reveals their positioning rules and migration dynamics at the atomic level.