Fig. 11
From: A beginner’s approach to deep learning applied to VS and MD techniques
A Overview of 2nd generation HDNNP architectures, in which the Cartesian coordinate vectors of the atoms of a system of interest get converted into a vector of ACSFs, to form the input for individual atomic NNs. These DFCNNs predict the potential energy of each separate atom in the system, delivering the total potential energy of the system when these atomic energies get tallied up. The atomic NNs are trained in the same manner per chemical element. B Overview of 3rd generation HDNNP architectures, expanding upon 2nd generation HDNNPs by not only using atomic NNs to predict atomic potential energies per atom of a system, but also atomic charge NNs to predict atomic electrostatic energies. This enables the NNPs to describe more long-range interactions in the system. The short-range potential energies and total electrostatic energies are summed up to provide the total energy of the system. The atomic charge NNs are trained in a different manner per chemical element than atomic NNs [153]