Abstract
<jats:p>Molecular dynamics (MD) provides a principled method for modeling equilibrium protein conformational energy landscapes, but its computational cost limits access to long timescales and larger protein systems. Recently, generative protein ensemble models and machine-learned coarse-grained force fields have emerged as complementary approaches for accelerating conformational sampling. However, they are typically developed separately despite modeling the same underlying equilibrium distribution. We introduce UniFlow, the first scalable generative model that unifies protein ensemble generation and machine-learned coarse-grained force fields for molecular dynamics simulation within a single framework. UniFlow employs an internal-coordinate normalizing flow that supports efficient i.i.d. sampling, exact likelihood evaluation, and differentiable energy and force computation. Across diverse protein systems, UniFlow generates ensembles that closely match reference MD simulations, generalizes to proteins beyond its training dataset, and samples substantially faster than diffusion-based ensemble-generation baselines. The same learned density further enables stable long-timescale molecular dynamics simulations. Together, UniFlow paves the way for a unified class of models that bridges generative ensemble modeling with physics-based molecular simulation.</jats:p>