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

2022/09/01 by Shingo Urata, Urata, Shingo · 2 citations
Materials Science · #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Glass properties and applications #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Phase-change materials and chalcogenides

paper · pdf · doi:10.48550/arxiv.2209.00316

openalex publication_date 2022/09/01 · openalex created_date 2022/09/03 · openalex updated_date 2026/07/28

Abstract

Lithium borosilicate (LBS) glass is a prototypical lithium-ion conducting oxide glasses available for an all-solid state buttery. Nevertheless, the atomistic modeling of LBS glass using ab initio (AIMD) and classical molecular dynamics (CMD) simulations have 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 fourhold-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.

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• Transferable potential for molecular dynamics simulations of borosilicate glasses and structural comparison of machine learning optimized parameters
• Optimization of Transferable Interatomic Potentials for Glasses toward Experimental Properties

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