  Deep potentials for molecular dynamics (MD) achieve first-principles accuracy
at much lower computational cost. However, their use in large length- and
time-scale simulations is limited by their lower speeds compared to analytical
atomistic potentials, primarily due to network complexity and long embedding
time. Here, based on the moments theorem, we develop a chemical-bonding-aware
embedding for neural network potentials that achieve state-of-the-art accuracy
in forces and local electronic density of states prediction with an ultrasmall
16x32 neural network resulting in significantly lower computational cost.

Materials Science;Computational Physics