DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space

We present <em>Deep learning for Collective Variables</em> (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.本研究推出《集体变量深度学习》（Deep learning for Collective Variables，简称DeepCV），一款可高效、自定义实现深度自编码器神经网络（deep autoencoder neural network，DAENN）算法的计算机程序。该算法由本课题组开发，用于计算集体变量（collective variables，CVs），可结合增强采样方法重构化学反应的自由能面。DeepCV可便捷实现分子特征计算、模型训练、集体变量生成、采样稀有事件验证，以及针对目标化学反应的轨迹分析。本研究以环己烯构象转变为例开展验证实验，实验中采用DAENN生成的集体变量进行元动力学模拟。结果显示，本研究所用集体变量计算得到的自由能，与此前已开发的集体变量所得结果及实验结果相符。DeepCV为基于TensorFlow框架、采用Python/C++面向对象语言编写的开源软件，可免费用于非商业用途，且可集成至通用分子动力学软件中。此外，DeepCV还附带多项配套工具，包括应用程序编程接口（application program interface，API）、文档与教程。