Free Energy Surface Sampling via Reduced Flow Matching
For researchers in statistical physics and computational chemistry, this method offers a more efficient way to sample free energy surfaces, which is crucial for understanding chemical reactions and conformational transitions.
FES-FM uses reduced flow matching to directly sample the free energy surface in collective variable space, drastically reducing computational costs while delivering superior accuracy per unit sampling time compared to traditional methods.
Sampling the free energy surface, namely, the distribution of collective variables (CVs), is a crucial problem in statistical physics, as it underpins a better understanding of chemical reactions and conformational transitions. Traditional methods for free energy surface sampling involve simulation in high-dimensional configuration space and projecting the resulting configurations onto the CV space. To reduce the computational costs of such sampling, we propose FES-FM, a reduced flow matching (FM) method for free energy sampling (FES). We train a dynamical transport map in the CV space, thereby enabling direct sampling of the free energy surface. For many-particle systems, we construct a prior distribution based on the Hessian at a local minimum of the potential, which ensures both rotation-translation invariance and physically meaningful configurations. We evaluate the proposed method across a variety of potential functions and collective variables. Comparative experiments demonstrate that our approach drastically reduces computational costs while delivering superior accuracy per unit sampling time.