An active learning workflow for predicting hydrogen atom adsorption energies on binary oxides based on local electronic transfer features

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外文摘要：Machine learning combined with density functional theory(DFT)enables rapid exploration of catalyst descriptors space such as adsorption energy,facilitating rapid and effective catalyst screening.However,there is still a lack of models for predicting adsorption energies on oxides,due to the complexity of elemental species and the ambiguous coordination environment.This work proposes an active learning workflow(LeNN)founded on local electronic transfer features(e)and the principle of coordinate rotation invariance.By accurately characterizing the electron transfer to adsorption site atoms and their surrounding geometric structures,LeNN mitigates abrupt feature changes due to different element types and clarifies coordination environments.As a result,it enables the prediction of*H adsorption energy on binary oxide surfaces with a mean absolute error(MAE)below 0.18 eV.Moreover,we incorporate local coverage(ql)and leverage neutral network ensemble to establish an active learning workflow,attaining a prediction MAE below 0.2 eV for 5419 multi-*H adsorption structures.These findings validate the universality and capability of the proposed features in predicting*H adsorption energy on binary oxide surfaces.

外文关键词：

Machine learningAdsorption energyBinary oxideElectron transferActive learning

作者：

Wenhao Jing、Zihao Jiao、Mengmeng Song、Ya Liu、Liejin Guo

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作者单位：

International Research Center for Renewable Energy,State Key Laboratory of Multiphase Flow in Power Engineering,Xi'an Jiaotong University,Shaanxi,710049,Xi'an,China

Department of Energy and Power Engineering,Shandong University of Technology,Zibo,255000,Shandong,China

基金：

National Natural Science Foundation of ChinaNatural Science Basic Research Program of ShaanxiKey Research and Development Program of ShaanxiFundamental Research Funds for the Central Universities

项目编号：

524882012024JC-YBMS-2842024GHYBXM-02

出版年：

2024

DOI：

10.1016/j.gee.2024.06.007

绿色能源与环境(英文)

CSTPCD

ISSN：

年,卷(期)：2024.9(10)