用自动编码器构造分子量子动力学网格
Constructing grids for molecular quantum dynamics using an autoencoder
摘要：
分子量子动力学（QD）计算的一个挑战是关于核自由度的维数灾难。对于快速反应过程来说，一种特别有效的常用方法是将系统的维度减少到几个最相关的坐标。识别这些可能会成为一项非常困难的任务，因为它们通常非常不直观。我们提出了一种机器学习方法，利用自动编码器，训练找到一组分子构型的低维表示。这些配置是通过对感兴趣的反应性分子系统进行轨迹计算而产生的。所得到的低维表示可以用于在期望的子空间中生成势能面网格。利用G-矩阵形式计算动能算符，可以在此网格上进行量子点计算。除了一步一步的指导网格建设，我们目前的应用程序的测试系统。
英文摘要：
A challenge for molecular quantum dynamics (QD) calculations is the curse of dimensionality with respect to the nuclear degrees of freedom. A common approach that works especially well for fast reactive processes is to reduce the dimensionality of the system to a few most relevant coordinates. Identifying these can become a very difficult task, since they often are highly unintuitive. We present a machine learning approach that utilizes an autoencoder that is trained to find a low-dimensional representation of a set of molecular configurations. These configurations are generated by trajectory calculations performed on the reactive molecular systems of interest. The resulting low-dimensional representation can be used to generate a potential energy surface grid in the desired subspace. Using the G-matrix formalism to calculate the kinetic energy operator, QD calculations can be carried out on this grid. In addition to step-by-step instructions for the grid construction, we present the application to a test system.
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