The shuttle effect has been a major obstacle to the development of lithium-sulfur batteries. The discovery of new host materials is essential, but lengthy and complex experimental studies are inefficient for the identification of potential host materials. We proposed a machine learning method for the rapid discovery of an AB2-type sulfur host material to suppress the shuttle effect using the 2DMatPedia database, discovering 14 new structures (PdN2, TaS2, PtN2, TaSe2, AgCl2, NbSe2, TaTe2, AgF2, NiN2, AuS2, TmI2, NbTe2, NiBi2, and AuBr2) from 1320 AB2-type compounds. These structures have strong adsorptions of greater than 1.0 eV for lithium polysulfides and appreciable electron-transportation capability, which can serve as the most promising AB2-type host materials in lithium-sulfur batteries. On the basis of a small data set, we successfully predicted Li2S6 adsorption at arbitrary sites on substrate materials using transfer learning, with a considerably low mean absolute error (below 0.05 eV). The proposed data-driven method, as accurate as density functional theory calculations, significantly shortens the research cycle of screening AB2-type sulfur host materials by approximately 8 years. This method provides high-precision and expeditious solutions for other high-throughput calculations and material screenings based on adsorption energy predictions.
Keywords: DFT; adsorption; lithium−sulfur batteries; machine learning; shuttle effect.