Journal
JOM
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.1007/s11837-023-06291-7
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Solid-state batteries are a transformative technology with the potential to redefine energy storage. However, their complex materials and interfaces pose significant challenges. This article highlights the integration of NASICON materials into solid-state battery architectures and the use of machine learning techniques to understand the structure-function relationships governing ion conductivity. The findings show the promise of NASICON-type materials in overcoming compatibility issues and enhancing battery performance.
Solid-state batteries (SSBs) represent a transformative technology with the potential to redefine energy storage in various applications, including portable electronics, electric vehicles, and renewable energy systems. However, the intricate interplay of materials and interfaces within SSBs poses significant challenges in realizing their full potential. In this perspective, we highlight the potential of integration of NASICON (Sodium Superionic Conductor) materials into SSB architectures to address compatibility issues and enhance overall performance. We also showcase that, by employing machine learning techniques and a data-driven approach, structure-function relationships governing NASICON materials' ionic conductivity can be perceived. This eliminates the need for computationally intensive first-principles calculations and offers insights into the mechanism of improving ion transport. Our findings highlight the promise of NASICON-type materials in overcoming chemical, mechanical, and electrochemical disparities at interfaces and interphases, offering a promising path forward for the development of high-performance SSBs.
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