Fig. 1. Behler and Parrinello's HDNN or HD-atomic NNP model. (A) A scheme showing the algorithmic structure of an atomic number specific neural network potential (NNP). The input molecular coordinates, q, are used to generate the atomic environment vector, G i X, for atom i with atomic number X. G i X is then fed into a neural network potential (NNP) trained specifically to predict atomic contributions, E i X, to the total energy, E T. Each l k represents a hidden layer of the neural network and is composed of nodes denoted a j k where j indexes the node. (B) The high-dimensional atomic NNP (HD-atomic NNP) model for a water molecule. G i X is computed for each atom in the molecule then input into their respective NNP (X) to produce each atom's E i X, which are summed to give E T.
