论文标题
从修改的高维神经网络中开发基于石墨烯的2D-3D接口的势能表面
Developing Potential Energy Surfaces for Graphene-based 2D-3D Interfaces from Modified High Dimensional Neural Networks for Applications in Energy Storage
论文作者
论文摘要
由二维（2D）和三维（3D）材料组成的混合二维异质结构是无可争议的下一代材料，用于工程设备，由于其可变特性。本工作在计算上研究了具有密度功能理论（DFT）方法的2D石墨烯和3D TIN（SN）系统之间的界面。它使用计算要求的模拟数据来开发基于机器学习（ML）的势能表面（PES）。已经讨论了根据有限的数据和此类模型的可传输性开发用于复杂界面系统的PE的方法。为了开发石墨烯键键界系统的PES，使用了高维神经网络（HDNN），这些神经网络（HDNN）依赖于以原子为中心的对称函数来表示结构信息。修改了HDNN以训练界面系统的总能量，而不是原子能。在5789个石墨烯的界面结构上训练的修改HDNN的性能在同一材料对的新接口上进行了测试，并具有不同级别的训练数据集结构偏差的水平。测试接口的根平方误差（RMSE）在0.01-0.45 eV/原子范围内，具体取决于参考训练数据集的结构偏差。通过避免将总能量分解为原子能的不正确分解，尽管数据集有限，但经过修改的HDNN模型被证明可获得更高的精度和可传递性。基于ML的建模方法的准确性提高了，有望在异质结构存储系统中设计界面具有更高的循环寿命和稳定性的成本效益手段。
Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally investigates the interface between 2D graphene and 3D tin (Sn) systems with density functional theory (DFT) method. It uses computationally demanding simulation data to develop machine learning (ML) based potential energy surfaces (PES). The approach to developing PES for complex interface systems in the light of limited data and transferability of such models has been discussed. To develop PES for graphene-tin interface systems, high dimensional neural networks (HDNN) are used that rely on atom-centered symmetry function to represent structural information. HDNN are modified to train on the total energies of the interface system rather than atomic energies. The performance of modified HDNN trained on 5789 interface structures of graphene|Sn is tested on new interfaces of the same material pair with varying levels of structural deviations from the training dataset. Root mean squared error (RMSE) for test interfaces fall in the range of 0.01-0.45 eV/atom, depending on the structural deviations from the reference training dataset. By avoiding incorrect decomposition of total energy into atomic energies, modified HDNN model is shown to obtain higher accuracy and transferability despite limited dataset. Improved accuracy in ML-based modeling approach promises cost-effective means of designing interfaces in heterostructure energy storage systems with higher cycle life and stability.
