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Lithium borosilicate (LBS) glass is a prototypical lithium-ion conductingoxide glasses available for an all-solid state buttery. Nevertheless, theatomistic modeling of LBS glass using $ab$ $initio$ (AIMD) and classicalmolecular dynamics (CMD) simulations have critical limitations due tocomputational cost and inaccuracy in reproducing the glass microstructures,respectively. To overcome these difficulties, a machine-learning potential(MLP) was examined in this work for modeling LBS glasses using DeepMD. Theglass structures obtained by this MLP possessed fourhold-coordinated boron($^4$B) confirmed well with the experimental data and abundance ofthree-membered rings. The models were energetically more stable compared withthose constructed with a functional force-field even though both the modelsincluded reasonable $^4$B. The results confirmed MLP to be superior to modelthe boron-containing glasses and address the inherent shortcomings of the AIMDand CMD. This study also discusses some limitations of MLP for modelingglasses.

Modeling Short-Range and Three-Membered Ring Structures in Lithium Borosilicate Glasses using Machine Learning Potential