Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
Abstract
Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost abinitio accuracy and the computational efficiency of empirical potentials. In this work we propose a machine learning method for constructing highdimensional potential energy surfaces based on feedforward 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 machinelearned potentials can be used for any further tasks, for example the optimization of molecular geometries, the calculation of rate constants or molecular dynamics.
 Publication:

arXiv eprints
 Pub Date:
 September 2021
 arXiv:
 arXiv:2109.07421
 Bibcode:
 2021arXiv210907421Z
 Keywords:

 Physics  Computational Physics;
 Statistics  Machine Learning
 EPrint:
 J. Chem. Theory Comput. 2020, 16, 8, 54105421