Latent Ewald summation for machine learning of long-range interactions

Message passing neural networks (MPNNs) [18-learn from reference quantum mechanical calculations 21] employ a number of graph convolution layers to communicate and then predict the energy and forces of atomic configurations information between atoms, thereby capturing quickly, thus allowing for a more accurate long-range interaction up to the local cutoff radius times and comprehensive exploration of material and molecular the number of layers. However, if parts of the system are properties at scale [1, 2]. Most state-of-the-art MLIP disconnected on the graph, e.g. two molecules with a distance methods use a short-range approximation: the effective beyond the cutoff, the message passing scheme does potential energy surface experienced by one atom is determined not help. A very interesting approach is the long-distance by its atomic neighborhood.