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

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 AB-type sulfur host material to suppress the shuttle effect using the database, discovering 14 new structures (PdN, TaS, PtN, TaSe, AgCl, NbSe, TaTe, AgF, NiN, AuS, TmI, NbTe, NiBi, and AuBr) from 1320 AB-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 AB-type host materials in lithium-sulfur batteries. On the basis of a small data set, we successfully predicted LiS 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 AB-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.