电子结构
Atom(片上系统)
人工神经网络
化学物理
紧密结合
原子轨道
材料科学
物理
分子物理学
计算机科学
凝聚态物理
人工智能
量子力学
电子
嵌入式系统
作者
Yang Huang,Shih‐Han Wang,Luke E. K. Achenie,Kamal Choudhary,Hongliang Xin
摘要
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 d-band characteristics of an active site, shedding light on the origin of free-atom-like d-states that are often observed in SAAs involving d10 metal hosts. This theory-infused neural network approach significantly enhances our understanding of the electronic properties of single-site catalytic materials beyond traditional theories.
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