Npj Comput. Mater.: 单原子催化剂—动态电荷转移模型

海归学者发起的公益学术平台

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交流学术，偶尔风月

来自西班牙巴塞罗那科技学院的Núria López教授领导的团队，提出了一种数据驱动的方法来获得一个简单的模型，可以识别衬底支撑单原子催化剂的动态电荷转移。作者发现，孤立的单金属原子和二氧化铈载体之间的动态电荷转移无处不在，并且可以通过描述金属-载体相互作用的数学表达式来预测动态电荷转移。他们为基于CeO₂的9-11族金属单原子催化剂创建了一个(701)基态和低激发态数据库，使用机器学习技术重现了所有的单原子催化剂的基态和激发态能量，然后使用主要特征来构建一个简约的、可解释的模型，该模型能够预测动态电子转移的出现，并合理化主要贡献。该模型选择了八个基本变量：原子序数、电离势、大小和金属配位、金属-氧键强度、表面应变和库仑相互作用，概括了以前的Born-Haber模型。其所提出的使用数据驱动的机器学习技术能够对金属-衬底相互作用中的动态电子转移进行预测，并为在单原子模型中引入复杂的电子结构铺平了道路。

Data-driven models for ground and excited states for Single Atoms on Ceria 

Julian Geiger, Albert Sabadell-Rendón, Nathan Daelman & Núria López 

Ceria-based single-atom catalysts present complex electronic structures due to the dynamic electron transfer between the metal atoms and the semiconductor oxide support. Understanding these materials implies retrieving all states in these electronic ensembles, which can be limiting if done via density functional theory. Here, we propose a data-driven approach to obtain a parsimonious model identifying the appearance of dynamic charge transfer for the single atoms (SAs). We first constructed a database of (701) electronic configurations for the group 9–11 metals on CeO2(100). Feature Selection based on predictive Elastic Net and Random Forest models highlights eight fundamental variables: atomic number, ionization potential, size, and metal coordination, metal–oxygen bond strengths, surface strain, and Coulomb interactions. With these variables a Bayesian algorithm yields an expression for the adsorption energies of SAs in ground and low-lying excited states. Our work paves the way towards understanding electronic structure complexity in metal/oxide interfaces.