$Machine Learning Potential for Copper Hydride Clusters: A Neutron Diffraction-Independent Approach for Locating Hydrogen Positions$

Machine Learning Potential for Copper Hydride Clusters: A Neutron Diffraction-Independent Approach for Locating Hydrogen Positions

Determining hydrogen positions in metal hydride clusters remains a formidable challenge, which relies heavily on unaffordable neutron diffraction. While machine learning has shown promise, only one deep learning-based method has been proposed so far, which relies heavily on neutron diffraction data...

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Bibliographic Details
Main Author:	Cong Fang (516517) (author)
Other Authors:	Zhuang Wang (4728315) (author), Ruixian Guo (18361287) (author), Yuxiao Ding (1626538) (author), Sicong Ma (1391395) (author), Xiaoyan Sun (28501) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified stochastic surface walking research fields constrained offers superior accuracy new research paradigm neural network ) alloy hydride systems ray diffraction data one deep learning neutron diffraction references neutron diffraction data unaffordable neutron diffraction metal hydride clusters copper hydride clusters machine learning potential nn proved effective neutron diffraction machine learning neutron sources universal solution shown promise relies heavily including silver hydrogen localization general applicability formidable challenge dft predictions dependent technique currently without based method also provides
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Machine Learning Potential for Copper Hydride Clusters: A Neutron Diffraction-Independent Approach for Locating Hydrogen Positions

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