类金刚石结构的分子动力学与人工神经网络耦合研究

Molecular Dynamics and Artificial Neural Network Coupling Study of DLC Structure

掺氮类金刚石薄膜(N-DLC)可以改善零件表面的摩擦学性能,近些年来对N-DLC薄膜摩擦学特性研究的热度居高不下。由于计算资源与计算机运行时间有限,难以获得大量数据对N-DLC薄膜摩擦实验中界面结构演化规律进行微观模拟。为了探究分子动力学和人工神经网络交叉使用的可行性,全面了解N-DLC的摩擦学性质及规律,将BP神经网络、KELM神经网络引用到N-DLC的研究中。通过LAMMPS软件对N-DLC进行建模,将分子动力学模拟的数据作为人工神经网络的数据来源,对两种神经网络进行训练。利用验证样本对训练好的两种模型进行验证,将两种神经网络的预测结果进行对比,选出性能最佳的网络模型。结果表明,采用神经网络可以预测N-DLC内部杂化键的变化趋势,且效率更高,所需计算资源更少,在一定程度上可以代替分子动力学模拟结果,为人们提供进一步的分析判断。研究为促进分子动力学与人工神经网络两种方法的共同发展提供了有益探索。

Nitrogen-doped Diamond-Like Carbon thin films(N-DLC)can improve the frictional properties of part surfaces,and therefore,the research on the tribological properties of N-DLC thin films has been in high demand in recent years.Due to the limited amount of computational resources and computer running time,it is difficult to obtain a large amount of experimental data for studying the microscopic simulation of the interfacial structure evolution law in the friction experiments of N-DLC thin films.In order to explore the feasibility of cross use of molecular dynamics and artificial neural networks,and to gain a more comprehensive understanding of the friction properties and laws of N-DLC,BP neural networks,and KELM neural networks were referenced to the study of N-DLC.N-DLC is modeled using LAMMPS software,and molecular dynamics simulation data is used as the data source for artificial neural networks to train the two types of neural networks.Use validation samples to validate the two trained models,compare the prediction results of the two neural networks,and select the network model with the best performance.The results show that neural networks can predict the trend of hybrid bonds within N-DLC,with higher efficiency and less computational resources required.To a certain extent,it can replace the molecular dynamics simulation results to provide further analytical judgment.The study provides a useful exploration to promote the joint development of the two methods of molecular dynamics and artificial neural networks.