论文标题
使用机器学习潜力在锂硼硅酸盐玻璃中对短距离和三元环结构进行建模
Modeling Short-Range and Three-Membered Ring Structures in Lithium Borosilicate Glasses using Machine Learning Potential
论文作者
论文摘要
硼硅酸盐（LBS）玻璃是一种典型的锂离子，可用于全固态黄油的氧化物玻璃。然而，使用$ ab $ $ $ $ INITIO $（AIMD）和经典分子动力学（CMD）模拟对LBS玻璃的原子建模具有关键的局限性，这是由于计算成本和不准确性分别重现玻璃微观结构。为了克服这些困难，在这项工作中检查了使用DEEPMD对LBS玻璃进行建模的机器学习电位（MLP）。该MLP获得的玻璃结构具有四式协调的硼（$^4 $ b），证实了实验数据和丰富的三元环。与用功能强度场构建的模型相比，这些模型在能量上更稳定，尽管这两个型号都包括合理的$^4 $ b。结果证实，MLP优于模型含硼的眼镜，并解决了AIMD和CMD的固有缺点。这项研究还讨论了MLP建模玻璃的一些局限性。
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 ($^4$B) 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 $^4$B. 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.
