Machine Learning Derived Blueprint for Rational Design of the Effective Single-Atom Cathode Catalyst of the Lithium-Sulfur Battery

doi:10.1021/acs.jpclett.1c00927

IMR OpenIR

	Machine Learning Derived Blueprint for Rational Design of the Effective Single-Atom Cathode Catalyst of the Lithium-Sulfur Battery
	Lian, Zan 1,2; Yang, Min 1,2; Jan, Faheem 1,2; Li, Bo 1,2
通讯作者	Li, Bo(boli@imr.ac.cn)
	2021-07-29
发表期刊	JOURNAL OF PHYSICAL CHEMISTRY LETTERS
ISSN	1948-7185
卷号	12 期号:29 页码:7053-7059
摘要	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.
资助者	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
收录类别	SCI
语种	英语
资助项目	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研究方向	Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics
WOS类目	Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Atomic, Molecular & Chemical
WOS记录号	WOS:000680449800044
出版者	AMER CHEMICAL SOC
引用统计	被引频次：33[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/159564
专题	中国科学院金属研究所
通讯作者	Li, Bo
作者单位	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
推荐引用方式 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.