α-separable graph Hamiltonian network: A robust model for learning particle interactions in lattice systems.

We propose an α-separable graph Hamiltonian network (α-SGHN) that reveals complex interaction patterns between particles in lattice systems. Utilizing trajectory data, α-SGHN infers potential interactions without prior knowledge about particle coupling, overcoming the limitations of traditional graph neural networks that require predefined links. Furthermore, α-SGHN preserves all conservation laws during trajectory prediction.