Learning composable time-unconditional molecular energy landscapes for zero-shot environmental transfer

Diffusion and flow-matching models are now the standard generative approaches for molecular conformational ensembles, but their learned vector fields are non-conservative and yield no scalar potential. Energy-based formulations restore conservativity by taking forces as the gradient of a learned scalar, yet the landscapes they recover remain noise-conditional or are defined only up to an arbitrary scale. In neither case can they be added to a classical force field in physical units or composed with external environmental potentials inside a molecular dynamics engine. Classical potentials are useful precisely because environmental interactions enter additively: a solute potential can be moved between vacuum, solvent, and a protein binding pocket by summing it with a potential for the new environment. We introduce LEVI, a time-unconditional energy-based generative model that is trained by both score matching and an energy-regression term that aligns it to the physical energies carried by the data. We also introduce LEViMD, which composes the resulting potential with external environmental terms at every step of a molecular dynamics integrator, so that a single solute model transfers to environments it never saw during training. We validate this in two systems: a two-dimensional system where data lies on a known one-dimensional manifold, and on alanine dipeptide, where a learned potential trained on vacuum structures alone can be composed with explicit water at inference, recapitulating known conformational shifts between vacuum and water.