Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)

doi:10.1103/PhysRevLett.130.078001

IMR OpenIR

	Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
	Liu, Peitao 1,2; Wang, Jiantao 2; Avargues, Noah 1; Verdi, Carla 1; Singraber, Andreas 3; Karsai, Ferenc 3; Chen, Xing-Qiu 2; Kresse, Georg 1,3
通讯作者	Liu, Peitao(ptliu@imr.ac.cn)
	2023-02-17
发表期刊	PHYSICAL REVIEW LETTERS
ISSN	0031-9007
卷号	130 期号:130 页码:6
摘要	Adsorption of carbon monoxide (CO) on transition-metal surfaces is a prototypical process in surface sciences and catalysis. Despite its simplicity, it has posed great challenges to theoretical modeling. Pretty much all existing density functionals fail to accurately describe surface energies and CO adsorption site preference as well as adsorption energies simultaneously. Although the random phase approximation (RPA) cures these density functional theory failures, its large computational cost makes it prohibitive to study the CO adsorption for any but the simplest ordered cases. Here, we address these challenges by developing a machine-learned force field (MLFF) with near RPA accuracy for the prediction of coverage -dependent adsorption of CO on the Rh(111) surface through an efficient on-the-fly active learning procedure and a Delta-machine learning approach. We show that the RPA-derived MLFF is capable to accurately predict the Rh(111) surface energy and CO adsorption site preference as well as adsorption energies at different coverages that are all in good agreement with experiments. Moreover, the coverage -dependent ground-state adsorption patterns and adsorption saturation coverage are identified.
资助者	Austrian Science Fund (FWF) within the SFB TACO ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Science Fund for Distinguished Young Scholars
DOI	10.1103/PhysRevLett.130.078001
收录类别	SCI
语种	英语
资助项目	Austrian Science Fund (FWF) within the SFB TACO[F 81-N] ; National Natural Science Foundation of China[52201030] ; National Key R&D Program of China[2021YFB3501503] ; National Science Fund for Distinguished Young Scholars[51725103]
WOS研究方向	Physics
WOS类目	Physics, Multidisciplinary
WOS记录号	WOS:000977331900004
出版者	AMER PHYSICAL SOC
引用统计
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/177540
专题	中国科学院金属研究所
通讯作者	Liu, Peitao
作者单位	1.Univ Vienna, Fac Phys, Ctr Computat Mat Sci, Kolingasse 14-16, A-1090 Vienna, Austria 2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 3.VASP Software GmbH, Sensengasse 8, A-1090 Vienna, Austria
推荐引用方式 GB/T 7714	Liu, Peitao,Wang, Jiantao,Avargues, Noah,et al. Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)[J]. PHYSICAL REVIEW LETTERS,2023,130(130):6.
APA	Liu, Peitao.,Wang, Jiantao.,Avargues, Noah.,Verdi, Carla.,Singraber, Andreas.,...&Kresse, Georg.(2023).Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111).PHYSICAL REVIEW LETTERS,130(130),6.
MLA	Liu, Peitao,et al."Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)".PHYSICAL REVIEW LETTERS 130.130(2023):6.