Coarse-grained molecular dynamics (MD) simulation is a promising alternative to all-atom MD simulation for the fast calculation of system properties, which is imperative in designing materials with a specific target property. There have been several coarse-graining strategies developed over the past few years that provide accurate structural properties of the system. However, these coarse-grained models share a major drawback in that they introduce an artificial acceleration in molecular mobility. In this paper, we report a data-driven approach to generate coarse-grained models that preserve the all-atom molecular mobility. We designed a machine learning model in the form of an artificial neural network, which directly predicts the simulation-ready mobility-preserving coarse-grained potential as an output given the all-atom force field (FF) parameters as inputs. As a proof of principle, we took 2,3,4-trimethylpentane as a model system and described the development of machine learning models in detail. We quantify the artificial acceleration in molecular mobility by defining the acceleration factor as the ratio of the coarse-grained and the all-atom diffusion coefficient. The predicted coarse-grained potential generated by the best machine learning model can bring down the acceleration factor to a value of ∼2, which could be otherwise as large as 7 for a typical value of 3 × 10^–9 m² s^–1 for the all-atom diffusion coefficient. We believe our method will be of interest in the community as a route to generating coarse-grained potentials with accurate dynamics.

中文翻译：

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迈向保持移动性的粗粒度模型：一种数据驱动的方法

粗粒度分子动力学 (MD) 模拟是全原子 MD 模拟的一种有前途的替代方法，可用于快速计算系统特性，这在设计具有特定目标特性的材料时必不可少。在过去的几年里，已经开发了几种粗粒度策略，它们提供了系统的准确结构特性。然而，这些粗粒度模型的一个主要缺点是它们引入了分子运动的人为加速。在本文中，我们报告了一种数据驱动的方法来生成保留全原子分子流动性的粗粒度模型。我们设计了一个人工神经网络形式的机器学习模型，在给定全原子力场 (FF) 参数作为输入的情况下，它直接预测模拟就绪的迁移率保持粗粒度势能作为输出。作为原理证明，我们以 2,3,4-三甲基戊烷作为模型系统，详细描述了机器学习模型的开发。我们通过将加速因子定义为粗粒度和全原子扩散系数的比率来量化分子迁移率的人工加速。最好的机器学习模型生成的预测粗粒度潜力可以将加速因子降低到 ∼2 的值，否则对于 3 × 10 的典型值可能高达 7 我们通过将加速因子定义为粗粒度和全原子扩散系数的比率来量化分子迁移率的人工加速。最好的机器学习模型生成的预测粗粒度潜力可以将加速因子降低到 ∼2 的值，否则对于 3 × 10 的典型值可能高达 7 我们通过将加速因子定义为粗粒度和全原子扩散系数的比率来量化分子迁移率的人工加速。最好的机器学习模型生成的预测粗粒度潜力可以将加速因子降低到 ∼2 的值，否则对于 3 × 10 的典型值可能高达 7^–9 m ² s ^–1为全原子扩散系数。我们相信我们的方法会引起社区的兴趣，作为一种生成具有准确动态的粗粒度潜力的途径。