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Breakthrough in Screening and Discovering Cathode Materials for Lithium-Sulfur Batteries

November 16, 2020      Author:

Recently, a research team led by Li Jinjin from School of Electronic Information and Electrical Engineering, SJTU published their latest research findings in Energy Storage Materials (IF=16.28), a top journal in energy study. They proposed a machine learning method to rapidly and accurately predict the binding energies towards lithium polysulfides (LiPS), which greatly facilitated the screening and discovery of cathode materials for lithium-sulfur batteries. Their research greatly enhances the application of transfer learning in the area of complex materials by demonstrating that transfer learning can overcome the obstacle caused by a lack in material property data, which is of great importance to providing a general predicting model for research on the binding energies between two-dimensional layered materials and LiPS.

The first affiliation of this paper is School of Electronic Information and Electrical Engineering (EIEE), SJTU. The first authors are postgraduate student Zhang Haikuo and doctoral student Wang Zhilong from Department of MICRO/NANO Electronics. The corresponding authors are Professor Li Jinjin from Department of MICRO/NANO Electronics and Professor Liu Jinyun from Anhui Normal University. Their research has been sponsored by National Natural Science Foundation of China and SJTU.

 

ABSTRACT:

The shuttle effect of lithium polysulfides (LiPS) leads to fast capacity loss in lithium-sulfur batteries, which hinders the practical applications and makes the discovery of shuttle effect-suppressive sulfur host materials highly significant. Here, we proposed a machine learning (ML) method to rapidly and accurately predict the binding energies towards LiPS including Li2S4, Li2S6, and Li2S8 adsorbed on the surface of sulfur hosts with arbitrary configurations and active sites. As a case study, MoSe2 was selected as a sulfur host to predict the binding energy when absorbing the LiPS. The ML method shows six orders of magnitude faster than the conventional density functional theory (DFT), with a low predicted mean absolute error (MAE) of 0.1 eV. Based on the transfer learning (TL), we demonstrated that the presented ML method can be transferred to other layered compounds with a similar AB2 structure to MoSe2, and can efficiently predict their binding strengths with hosts. WSe2 was employed as a case to validate the TL method, with the results showing that MoSe2 had a stronger binding strength than WSe2 when adsorbing the LiPS, and only one-seventh of the ML training data was required. The impacts of different adsorption sites, configurations and distances on the binding energy were analyzed when LiPS is absorbed, which is of great significance to understand the adsorption mechanism of LiPS with hosts. The proposed work provides an efficient ML method to screen and discover new AB2 typed two-dimensional layered materials for suppressing the shuttle effect in lithium-sulfur battery.

 

Author: School of Electronic Information and Electrical Engineering

Affiliation: School of Electronic Information and Electrical Engineering