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arXiv · 1710.04535

Constructing grids for molecular quantum dynamics using an autoencoder

Julius P. P. Zauleck·Regina de Vivie-Riedle
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Abstract

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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Julius P. P. Zauleck, Regina de Vivie-Riedle. 2017-10-12. Constructing grids for molecular quantum dynamics using an autoencoder. https://doi.org/10.1021/acs.jctc.7b01045

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