Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India.
Center for Visual Information Technology, KCIS, International Institute of Information Technology, Hyderabad 500 032, India.
J Phys Chem A. 2020 Aug 27;124(34):6954-6967. doi: 10.1021/acs.jpca.0c03926. Epub 2020 Aug 14.
The computationally expensive nature of molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a Δ-NetFF machine learning model, where the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields, was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.
分子动力学模拟的计算成本很高，严重限制了其模拟大系统规模和长时间尺度的能力，而这两者对于模拟实验条件都是必要的。在这项工作中，我们探索了一种方法，即利用量子力学密度泛函理论（DFT）在小系统上获得的数据，并通过以液态氩气为测试案例，利用深度学习来模拟大系统。选择了合适的向量表示来表示每个 Ar 原子的周围环境，并引入了一个 Δ-NetFF 机器学习模型，其中神经网络被训练来预测 DFT 和经典力场得到的结果力之间的差异。然后，根据我们计算的性质，使用神经网络的力对各种系统规模和时间尺度进行分子动力学模拟。提供了从经典力场和神经网络模型获得的性质与可用实验数据的比较，以验证所提出的方法。
