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Machine-Learned Coarse-Grained Models
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2018-07-19 00:00:00 , DOI: 10.1021/acs.jpclett.8b01416 Karteek K. Bejagam 1 , Samrendra Singh 2 , Yaxin An 1 , Sanket A. Deshmukh 1
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2018-07-19 00:00:00 , DOI: 10.1021/acs.jpclett.8b01416 Karteek K. Bejagam 1 , Samrendra Singh 2 , Yaxin An 1 , Sanket A. Deshmukh 1
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Optimizing force-field (FF) parameters to perform molecular dynamics (MD) simulations is a challenging and time-consuming process. We present a novel FF optimization framework that integrates MD simulations with particle swarm optimization (PSO) algorithm and artificial neural network (ANN). This new ANN-assisted PSO framework was used to develop transferable coarse-grained (CG) models for D2O and DMF as a proof of concept. The PSO algorithm was used to generate the set of input FF parameters for the MD simulations of the CG models of these solvents, which were optimized to reproduce their experimental properties. Herein, for the first time, a reverse approach was employed for on-the-fly training of the ANN model, where results (solvent properties) obtained from the MD simulations and their corresponding FF parameters were used as inputs and outputs, respectively. The ANN model was then required to predict a set of new FF parameters, which were tested for their ability to predict the desired experimental properties. This new framework can be extended to integrate any optimization algorithm with ANN and MD simulations to accelerate the FF development.
中文翻译：
机器学习的粗粒度模型
优化力场（FF）参数以执行分子动力学（MD）模拟是一个具有挑战性和耗时的过程。我们提出了一个新颖的FF优化框架，该框架将MD模拟与粒子群优化（PSO）算法和人工神经网络（ANN）集成在一起。这个新的ANN辅助PSO框架用于为D 2开发可转移的粗粒度（CG）模型O和DMF作为概念证明。PSO算法用于为这些溶剂的CG模型的MD模拟生成一组输入FF参数，并对其进行优化以重现其实验性质。本文中，首次采用了一种反向方法进行ANN模型的动态训练，其中从MD模拟及其相应的FF参数获得的结果（溶剂性质）分别用作输入和输出。然后需要ANN模型来预测一组新的FF参数，并对其进行预测所需实验特性的能力进行测试。可以扩展此新框架，以将任何优化算法与ANN和MD仿真集成在一起，以加快FF的开发速度。
更新日期：2018-07-19
中文翻译：
机器学习的粗粒度模型
优化力场（FF）参数以执行分子动力学（MD）模拟是一个具有挑战性和耗时的过程。我们提出了一个新颖的FF优化框架，该框架将MD模拟与粒子群优化（PSO）算法和人工神经网络（ANN）集成在一起。这个新的ANN辅助PSO框架用于为D 2开发可转移的粗粒度（CG）模型O和DMF作为概念证明。PSO算法用于为这些溶剂的CG模型的MD模拟生成一组输入FF参数，并对其进行优化以重现其实验性质。本文中，首次采用了一种反向方法进行ANN模型的动态训练，其中从MD模拟及其相应的FF参数获得的结果（溶剂性质）分别用作输入和输出。然后需要ANN模型来预测一组新的FF参数，并对其进行预测所需实验特性的能力进行测试。可以扩展此新框架，以将任何优化算法与ANN和MD仿真集成在一起，以加快FF的开发速度。
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