论文标题
通过深度神经网络学习方法开发AL-TB合金的原子间潜力
Development of Interatomic Potential for Al-Tb Alloy by Deep Neural Network Learning Method
论文作者
论文摘要
使用深神经网络（DNN）学习方法开发了围绕AL90TB10组成的Al-TB合金的原子间潜力。收集了从头算分子动力学（AIMD）模拟获得的每个原子上的原子构型以及相应的总势能和力，以训练DNN模型，以构建Al-TB合金的原子间潜力。我们显示获得的DNN模型可以很好地再现AIMD计算的能量和力。与AIMD的结果相比，使用DNN间原子电位的分子动力学（MD）模拟还准确地描述了AL90TB10液体的结构特性，例如部分对相关函数（PPCF）和键角分布。此外，开发的DNN间原子势可以预测AL-TB系统晶体相的形成能，其精度与Ab ISTILE计算相当。通过MD模拟使用开发的DNN间原子势获得的AL90TB10金属玻璃的结构因子也与实验X射线衍射数据一致。
An interatomic potential for Al-Tb alloy around the composition of Al90Tb10 was developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total potential energies and forces on each atom obtained from ab initio molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the interatomic potential for Al-Tb alloy. We show the obtained DNN model can well reproduce the energies and forces calculated by AIMD. Molecular dynamics (MD) simulations using the DNN interatomic potential also accurately describe the structural properties of Al90Tb10 liquid, such as the partial pair correlation functions (PPCFs) and the bond angle distributions, in comparison with the results from AIMD. Furthermore, the developed DNN interatomic potential predicts the formation energies of crystalline phases of Al-Tb system with the accuracy comparable to ab initio calculations. The structure factor of Al90Tb10 metallic glass obtained by MD simulation using the developed DNN interatomic potential is also in good agreement with the experimental X-ray diffraction data.
