Abstract
Machine learning in materials science and solid-state batteries are two topics that have captured the imagination of researchers in recent years. Therefore, it is unsurprising that many researchers have attempted to apply the advances in ML to the discovery and study of materials for solid-state batteries. In this talk, I will discuss the importance of going back to fundamentals in the application of ML to materials for solid-state batteries, in particular, the critical solid electrolyte component and its interfaces. I will highlight areas where ML has had a transformative impact on our understanding and discovery of solid electrolytes, and what are some of the remaining challenges that remain to be surmounted.
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