aceApplications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries
Solid-state electrolytes (SSEs) have attracted considerable attention for their ability to effectively suppress lithium dendrite growth and enhance the safety and life cycle of lithium-ion batteries (LIBs). However, the commercialization of SSEs has been hindered by low ionic conductivity, limited mechanical strength, and poor interfacial compatibility. Recently, machine learning (ML) has arisen as a helpful tool in SSE studies owing to its efficient data processing and pattern recognition capabilities. This pap…
Machine learning accelerates solid-state electrolyte development by efficiently predicting ionic conductivity, elastic moduli, and thermodynamic stability to enable rapid next-generation design.
Model predictive performance is strongly dependent on descriptor selection, and interpretability and evaluation remain active considerations for deployment.
Evidence
- Peer-reviewedACS Omega2025-12-01
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Truvace Impact Record TRV-2026-0441, v1: “Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries.” Truvace, 2026-07-20. /record/TRV-2026-0441 (accessed at citation time). sha256 050b15f28dcb5518…
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