Using neural network potentials to study defect formation and phonon properties of nitrogen vacancies with multiple charge states in GaN
Using neural network potentials to study defect formation and phonon properties of nitrogen vacancies with multiple charge states in GaN
复制标题
利用神经网络势研究 GaN 中多电荷态氮空位的缺陷形成和声子特性
DOI：
10.1103/physrevb.106.054108
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发表时间：
2022
影响因子：
3.7
通讯作者：
Watanabe Satoshi
中科院分区：
文献类型：
--
作者：
Shimizu Koji;Dou Ying;Arguelles Elvis F.;Moriya Takumi;Minamitani Emi;Watanabe Satoshi
对带电缺陷的研究对于理解半导体的性质是必要的。虽然密度泛函理论计算可以准确地描述相关的物理量，但这些计算大大增加了计算量，这往往限制了该方法在大系统中的应用。在本文中，我们提出了一种不同的神经网络势(NNP)方案来分析多电荷态下的点缺陷行为。所提出的方案只需要对传统方案进行很小的修改。我们利用纤锌矿-GaN中的N空位对NNP进行了预测，结果表明NNP具有0、、和的电荷态。该方案准确地训练了所有电荷态的总能量和原子力。此外，它还很好地再现了缺陷结构的声子能带结构和热力学性质。基于这篇论文的结果，我们期望所提出的方案能够使我们能够研究更复杂的缺陷系统，并导致在新的半导体应用方面的突破。
Investigation of charged defects is necessary to understand the properties of semiconductors. Whereas density functional theory calculations can accurately describe the relevant physical quantities, these calculations increase the computational loads substantially, which often limits the application of this method to large-scale systems. In this paper, we propose a different scheme of neural network potential (NNP) to analyze the point defect behavior in multiple charge states. The proposed scheme necessitates only minimal modifications to the conventional scheme. We demonstrated the prediction performance of the proposed NNP using wurzite-GaN with a nitrogen vacancy with charge states of 0,,, and. The proposed scheme accurately trained the total energies and atomic forces for all the charge states. Furthermore, it fairly reproduced the phonon band structures and thermodynamics properties of the defective structures. Based on the results of this paper, we expect that the proposed scheme can enable us to study more complicated defective systems and lead to breakthroughs in novel semiconductor applications.