AI-Aided Mapping of the Structure-Composition-Conductivity Relationships of Glass-Ceramic Lithium Thiophosphate Electrolytes
Haoyue Guo, Qian Wang, Alexander Urban, Nongnuch Artrith

TL;DR
This study uses AI and first-principles methods to map the structure-property relationships in glass-ceramic lithium thiophosphate electrolytes, identifying compositions with high ionic conductivity for solid-state batteries.
Contribution
It introduces an integrated AI and computational approach to understand and predict the structure and conductivity of glass-ceramic electrolytes, proposing a new high-conductivity composition.
Findings
Identified composition with >10^{-2} S/cm conductivity.
Mapped the phase diagram linking structure and composition.
Proposed a candidate electrolyte with optimized properties.
Abstract
Lithium thiophosphates (LPS) with the composition (LiS)(PS) are among the most promising prospective electrolyte materials for solid-state batteries (SSBs), owing to their superionic conductivity at room temperature ( S cm), soft mechanical properties, and low grain boundary resistance. Several glass-ceramic (gc) LPS with different compositions and good Li conductivity have been previously reported, but the relationship between composition, atomic structure, stability, and Li conductivity remains unclear due to the challenges in characterizing non-crystalline phases in experiments or simulations. Here, we mapped the LPS phase diagram by combining first principles and artificial intelligence (AI) methods, integrating density functional theory, artificial neural network potentials, genetic-algorithm sampling, and ab initio molecular dynamics…
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Taxonomy
TopicsAdvanced Battery Materials and Technologies · Advancements in Battery Materials · Solid-state spectroscopy and crystallography
