  The combination of neural network potential (NNP) with molecular simulations
plays an important role in an efficient and thorough understanding of a
molecular system's potential energy surface (PES). However, grasping the
interplay between input features and their local contribution to NNP is
growingly evasive due to heavy featurization. In this work, we suggest an
end-to-end model which directly predicts per-atom energy from the coordinates
of particles, avoiding expert-guided featurization of the network input.
Employing self-attention as the main workhorse, our model is intrinsically
equivariant under the permutation operation, resulting in the invariance of the
total potential energy. We tested our model against several challenges in
molecular simulation problems, including periodic boundary condition (PBC),
$n$-body interaction, and binary composition. Our model yielded stable
predictions in all tested systems with errors significantly smaller than the
potential energy fluctuation acquired from molecular dynamics simulations.
Thus, our work provides a minimal baseline model that encodes complex
interactions in a condensed phase system to facilitate the data-driven analysis
of physicochemical systems.
