| Machine Learning Derived Blueprint for Rational Design of the Effective Single-Atom Cathode Catalyst of the Lithium-Sulfur Battery | |
| Lian, Zan1,2; Yang, Min1,2; Jan, Faheem1,2; Li, Bo1,2 | |
| Corresponding Author | Li, Bo(boli@imr.ac.cn) |
| 2021-07-29 | |
| Source Publication | JOURNAL OF PHYSICAL CHEMISTRY LETTERS
 |
| ISSN | 1948-7185 |
| Volume | 12Issue:29Pages:7053-7059 |
| Abstract | The "shuttle effect" and sluggish kinetics at cathode significantly hinder the further improvements of the lithium-sulfur (Li- s) battery, a candidate of next generation energy storage technolo Herein, machine learning based on high-throughput density functional theory calculations is employed to establish the pattern of polysulfides adsorption and screen the supported single-atom catalyst (SAC). The adsorptions are classified as two categories which successfully distinguish S-S bond breaking from the others. Moreover, a general trend of polysulfides adsorption was established regarding of both kind of metal and the nitrogen configurations on support. The regression model has a mean absolute error of 0.14 eV which exhibited a faithful predictive ability. Based on adsorption energy of soluble polysulfides and overpotential, the most promising SAC was proposed, and a volcano curve was found. In the end, a reactivity map is supplied to guide SAC design of the Li-S battery. |
| Funding Organization | National Natural Science Foundation of China ; Joint Research Fund Liaoning-Shenyang National Laboratory for Materials Science ; State Key Laboratory of Catalytic Materials and Reaction Engineering (RIPP) ; Special Program for Applied Research on Super Computation of the NSFC Guangdong Joint Fund (the second phase) |
| DOI | 10.1021/acs.jpclett.1c00927 |
| Indexed By | SCI |
| Language | en |
| Funding Project | National Natural Science Foundation of China[21573255] ; Joint Research Fund Liaoning-Shenyang National Laboratory for Materials Science ; State Key Laboratory of Catalytic Materials and Reaction Engineering (RIPP) ; Special Program for Applied Research on Super Computation of the NSFC Guangdong Joint Fund (the second phase)[U1501501] |
| WOS Research Area | Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics |
| WOS Subject | Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Atomic, Molecular & Chemical |
| WOS ID | WOS:000680449800044 |
| Publisher | AMER CHEMICAL SOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/159564 |
| Collection | 中国科学院金属研究所 |
| Corresponding Author | Li, Bo |
| Affiliation | 1.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Liaoning, Peoples R China 2.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Liaoning, Peoples R China |
| Recommended Citation GB/T 7714 | Lian, Zan,Yang, Min,Jan, Faheem,et al. Machine Learning Derived Blueprint for Rational Design of the Effective Single-Atom Cathode Catalyst of the Lithium-Sulfur Battery[J]. JOURNAL OF PHYSICAL CHEMISTRY LETTERS,2021,12(29):7053-7059. |
| APA | Lian, Zan,Yang, Min,Jan, Faheem,&Li, Bo.(2021).Machine Learning Derived Blueprint for Rational Design of the Effective Single-Atom Cathode Catalyst of the Lithium-Sulfur Battery.JOURNAL OF PHYSICAL CHEMISTRY LETTERS,12(29),7053-7059. |
| MLA | Lian, Zan,et al."Machine Learning Derived Blueprint for Rational Design of the Effective Single-Atom Cathode Catalyst of the Lithium-Sulfur Battery".JOURNAL OF PHYSICAL CHEMISTRY LETTERS 12.29(2021):7053-7059. |
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