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

Zan Lian; Min Yang; Faheem Jan; Bo Li

doi:10.1021/acs.jpclett.1c00927

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

J Phys Chem Lett. 2021 Jul 29;12(29):7053-7059. doi: 10.1021/acs.jpclett.1c00927. Epub 2021 Jul 22.

Authors

Zan Lian^{1

2}, Min Yang^{1

2}, Faheem Jan^{1

2}, Bo Li^{1

2}

Affiliations

¹ Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, Liaoning, People's Republic of China.
² School of Materials Science and Engineering, University of Science and Technology of China, Shenyang 110016, Liaoning, People's Republic of China.

PMID: 34291938
DOI: 10.1021/acs.jpclett.1c00927

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 technology. 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.