论文标题
具有多粒子输入的MD-GAN：从短时MD数据中的长期分子行为学习的机器学习
MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data
论文作者
论文摘要
MD-GAN是一种基于机器学习的方法，可以在任何时间步骤中发展系统的一部分，从而加速了分子动力学数据的生成。为了准确预测MD-GAN，应将有关系统动力学的足够信息包括在训练数据中。因此，系统的选择对于有效学习至关重要。在先前的研究中，仅提取每个分子的一个粒子（或载体）作为系统的一部分。因此，我们研究了将信息从其他粒子添加到学习过程的有效性。在聚乙烯系统的实验中，当使用每个分子的三个粒子的动力学时，与单粒子输入相比，使用训练数据的三分之一时间长度成功预测了扩散。令人惊讶的是，还预测了使用此方法预测训练数据中扩散的未观察到的过渡。
MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection of the part of the system is important for efficient learning. In a previous study, only one particle (or vector) of each molecule was extracted as part of the system. Therefore, we investigated the effectiveness of adding information from other particles to the learning process. In the experiment of the polyethylene system, when the dynamics of three particles of each molecule were used, the diffusion was successfully predicted using one-third of the time length of the training data, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method.
