硼酚
密度泛函理论
理论(学习稳定性)
化学
人工神经网络
结构稳定性
化学物理
集合(抽象数据类型)
分子动力学
方向(向量空间)
曲面(拓扑)
匹配(统计)
生物系统
统计物理学
计算化学
计算机科学
人工智能
物理
几何学
机器学习
工程类
统计
生物
结构工程
数学
程序设计语言
作者
Pierre Mignon,Abdul‐Rahman Allouche,Neil Richard Innis,Colin Bousige
摘要
We developed a high-dimensional neural network potential (NNP) to describe the structural and energetic properties of borophene deposited on silver. This NNP has the accuracy of density functional theory (DFT) calculations while achieving computational speedups of several orders of magnitude, allowing the study of extensive structures that may reveal intriguing moiré patterns or surface corrugations. We describe an efficient approach to constructing the training data set using an iterative technique known as the “adaptive learning approach”. The developed NNP is able to produce, with excellent agreement, the structure, energy, and forces obtained at the DFT level. Finally, the calculated stability of various borophene polymorphs, including those not initially included in the training data set, shows better stabilization for ν ∼ 0.1 hole density, and in particular for the allotrope α (ν=1/9). The stability of borophene on the metal surface is shown to depend on its orientation, implying structural corrugation patterns that can be observed only from long-time simulations on extended systems. The NNP also demonstrates its ability to simulate vibrational densities of states and produce realistic structures with simulated STM images closely matching the experimental ones.
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