Journal
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 12, Issue 29, Pages 7053-7059Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.1c00927
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Funding
- 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]
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Machine learning based on high-throughput density functional theory calculations was used to establish the pattern of polysulfides adsorption and screen the supported single-atom catalyst (SAC). The adsorptions were classified into two categories distinguishing S-S bond breaking from the others, and a general trend of polysulfides adsorption regarding both kind of metal and nitrogen configurations on support was established with good predictive ability.
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.
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