Machine-Learning-Enabled Tricks of the Trade for Rapid Host Material Discovery in Li–S Battery

Abstract

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 <i>2DMatPedia</i> 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.