Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials

Journal of Chemical Theory and Computation
V Zaverkin, J Kästner

Abstract

Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab initio accuracy and the computational efficiency of empirical potentials. In this work, we propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. As input to the neural network, we propose an extendable invariant local molecular descriptor constructed from geometric moments. Their formulation via pairwise distance vectors and tensor contractions allows a very efficient implementation on graphical processing units (GPUs). The atomic species is encoded in the molecular descriptor, which allows the restriction to one neural network for the training of all atomic species in the data set. We demonstrate that the accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models. Due to its high accuracy and efficiency, the proposed machine-learned potentials can be used for any further tasks, for example, the optimization of molecular geometries, the calculation of rate constants, or molecular dynamics.

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Citations

Oct 3, 2020·The Journal of Chemical Physics·Jigyasa NigamMichele Ceriotti
Jan 5, 2021·Journal of Chemical Theory and Computation·Ruocheng HanSandra Luber
Apr 4, 2021·The Journal of Chemical Physics·Sebastian J R LeeThomas F Miller
Nov 20, 2020·Chemical Reviews·Julia Westermayr, Philipp Marquetand
Aug 17, 2021·Chemical Reviews·Volker L DeringerGábor Csányi
Oct 7, 2020·Chemical Reviews·Sergei Manzhos, Tucker Carrington
Sep 30, 2021·Journal of Chemical Theory and Computation·Viktor ZaverkinJohannes Kästner
Nov 5, 2021·Journal of Chemical Theory and Computation·Dávid Péter KovácsGábor Csányi

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