HALOM Method: An Efficient and Accurate Neutron Diffraction‐Independent Approach for Determining Hydrogen Locations in Metal Hydride Nanoclusters

Abstract Metal hydride nanoclusters play important roles in catalytic transformations and energy conversion. However, accurate localization of hydrogen atoms in these nanoclusters remains a grand challenge. Traditional techniques face inherent limitations that single‐crystal X‐ray diffraction cannot localize hydrides effectively, while neutron diffraction requires large single crystals and has limited accessibility. To address this challenge, we developed an innovative machine‐learning‐assisted hydrogen atom locator for metal hydrides (HALOM) algorithm for rapid and accurate hydrogen position prediction. This approach systematically searches potential hydrogen atom locations using a predefined potential function and refining these locations to minimize the total energy, which operates independently of neutron diffraction data and avoids exhaustive exploration of the potential energy surface (PES). In terms of prediction accuracy, HALOM fully reproduces neutron diffraction (ND)‐identified hydride sites and aligns perfectly with density functional theory (DFT) models, and even yields lower‐energy structures than literature DFT placements in some key systems (e.g., 2.86 eV lower for Cu 81 H 32 ). HALOM also demonstrates superior computational efficiency, which generates low‐energy hydrogen configurations in only seconds, compared to the expensive PES exploration for hours. Overall, HALOM offers an efficient and accurate tool for hydrogen localization in metal hydride nanoclusters to advance their structure‐property research.