Lithium borosilicate (LBS) glass is a prototypical lithium-ion
conducting oxide glass available for an all-solid-state buttery. Nevertheless,
the atomistic modeling of LBS glass using ab initio (AIMD) and classical molecular dynamics (CMD) simulations has critical
limitations due to computational 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. The glass structures obtained by this MLP
possessed 4-fold coordinated boron (4B) confirmed well
with the experimental data and abundance of three-membered rings.
The models were energetically more stable compared with those constructed
with a functional force field even though both the models included
reasonable 4B. The results confirmed MLP to be superior
to model the boron-containing glasses and address the inherent shortcomings
of the AIMD and CMD. This study also discusses some limitations of
MLP for modeling glasses.