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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 AuthorLi, Bo(boli@imr.ac.cn)
2021-07-29
Source PublicationJOURNAL OF PHYSICAL CHEMISTRY LETTERS
ISSN1948-7185
Volume12Issue:29Pages:7053-7059
AbstractThe "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 OrganizationNational 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)
DOI10.1021/acs.jpclett.1c00927
Indexed BySCI
Languageen
Funding ProjectNational 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 AreaChemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
WOS SubjectChemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Atomic, Molecular & Chemical
WOS IDWOS:000680449800044
PublisherAMER CHEMICAL SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/159564
Collection中国科学院金属研究所
Corresponding AuthorLi, Bo
Affiliation1.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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