Predicting Reactivity and Passivation of Solid-State Battery Interfaces

In this work, we build a computationally inexpensive, data-driven model that utilizes atomistic structure information to predict the reactivity of interfaces between any candidate solid-state electrolyte material and a Li metal anode. This model is trained on data from ab initio molecular dynamics (...

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Veröffentlicht in:	ACS applied materials & interfaces 2024-09, Vol.16 (38), p.51584-51594
Hauptverfasser:	Lomeli, Eder G., Ransom, Brandi, Ramdas, Akash, Jost, Daniel, Moritz, Brian, Sendek, Austin D., Reed, Evan J., Devereaux, Thomas P.
Format:	Artikel
Sprache:	eng
Schlagworte:	AIMD DFT ENERGY STORAGE machine learning materials discovery solid electrolytes solid-state batteries Surfaces, Interfaces, and Applications
Online-Zugang:	Volltext
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