Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111)
文献类型：期刊论文
| 作者 | Liu, Peitao1,3; Wang, Jiantao3; Avargues, Noah1; Verdi, Carla1; Singraber, Andreas2; Karsai, Ferenc2; Chen, Xing-Qiu3; Kresse, Georg1,2 |
| 刊名 | PHYSICAL REVIEW LETTERS
 |
| 出版日期 | 2023-02-17 |
| 卷号 | 130期号:130页码:6 |
| ISSN号 | 0031-9007 |
| DOI | 10.1103/PhysRevLett.130.078001 |
| 通讯作者 | Liu, Peitao(ptliu@imr.ac.cn) |
| 英文摘要 | 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000977331900004 |
| 出版者 | AMER PHYSICAL SOC |
| 资助机构 | 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 |
| 源URL | [http://ir.imr.ac.cn/handle/321006/177539]  |
| 专题 | 金属研究所_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.VASP Software GmbH, Sensengasse 8, A-1090 Vienna, Austria 3.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China |
| 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. |
入库方式： OAI收割
来源：金属研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。