Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development.

ABSTRACT: The practical development of Li | |S batteries is hindered by the slow kinetics of polysulfides conversion reactions during cycling. To circumvent this limitation, researchers suggested the use of transition metal-based electrocatalytic materials in the sulfur-based positive electrode. However, the atomic-level interactions among multiple electrocatalytic sites are not fully understood. Here, to improve the understanding of electrocatalytic sites, we propose a multi-view machine-learned framework to evaluate electrocatalyst features using limited datasets and intrinsic factors, such as corrected d orbital properties. Via physicochemical characterizations and theoretical calculations, we demonstrate that orbital coupling among sites induces shifts in band centers and alterations in the spin

SUBMITTER: Han Z 

PROVIDER: S-EPMC11541723 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature