Huang, Y.
, Wang, S.
, Achenie, L.
, Choudhary, K.
and Xin, H.
(2024),
Origin of unique electronic structures of single-atom alloys unravelled by interpretable deep learning, Journal of Chemical Physics, [online], https://doi.org/10.1063/5.0232141, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958485
(Accessed October 10, 2025)
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Origin of unique electronic structures of single-atom alloys unravelled by interpretable deep learning
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Author(s)
Yang Huang, Shih-Han Wang, Luke Achenie, , Hongliang Xin
Abstract
We uncover the origin of unique electronic structures of single-atom alloys (SAAs) by interpretable deep learning. The approach integrates tight-binding moment theory with graph neural networks to accurately describe the local electronic structure of transition and noble metal sites upon perturbation. We emphasize the complex interplay of interatomic orbital coupling and on-site orbital resonance, which shapes the \textitd}-band characteristics of an active site, shedding light on the origin of free-atom-like \textitd}-states that are often observed in SAAs involving \textitd}$^10}$ metal hosts. This theory-infused neural network approach significantly enhances our understanding of the electronic properties of single-site catalytic materials beyond traditional theories.Citation
Journal of Chemical Physics
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Created October 21, 2024, Updated November 4, 2024