  Flow-based generative models have been employed for sampling the Boltzmann
distribution, but their application to high-dimensional systems is hindered by
the significant computational cost of obtaining the Jacobian of the flow. To
overcome this challenge, we introduce the flow perturbation method, which
incorporates optimized stochastic perturbations into the flow. By reweighting
trajectories generated by the perturbed flow, our method achieves unbiased
sampling of the Boltzmann distribution with orders of magnitude speedup
compared to both brute force Jacobian calculations and the Hutchinson
estimator. Notably, it accurately sampled the Chignolin protein with all atomic
Cartesian coordinates explicitly represented, which, to our best knowledge, is
the largest molecule ever Boltzmann sampled in such detail using generative
models.
