Exploration of Lithium-Ion Conductors Based on Local Coordination Environments Using Crystallographic Site Fingerprints.

The development of high-performance solid-state electrolytes for Li-ion batteries represents a critical challenge because many potential Li-containing compounds remain unexplored. In order to overcome this challenge, in this study, we utilized a semisupervised learning approach to streamline the discovery of novel Li-ion conductors by focusing on local coordination environments. Herein, we introduced four structure-representation descriptors to represent local coordination and applied agglomerative clustering to a data set of 3,835 Li-containing structures. The clusters were subsequently labeled with available experimentally determined ionic conductivity values to assess the efficacy of these descriptors in identifying promising conductors. After screening the obtained high-conductivity clusters and their neighboring structures, we shortlisted 147 compounds, which were further evaluated by molecular dynamics simulations to identify Li(3)LaP(2)S(8) as a potential candidate. Li(3)LaP(2)S(8) experimentally displayed low conductivity; however, optimizing the lithium content yielded Li(3.1)La(0.9)Sr(0.1)P(2)S(8), which showed a conductivity of 2.1 × 10(-6) S cm(-1) at 298 K. To the best of our knowledge, this is the first reported investigation of Li(3)LaP(2)S(8) as a solid-state electrolyte and highlights the power of semisupervised learning in accelerating the discovery of advanced materials. Our findings provide a valuable methodology for developing next-generation solid-state battery technologies.