TL/DR: Invertible generative models can be used to generate equilibrium states from high-dimensional multi-body systems such as proteins with hundreds of atoms. Training is a mixture of likelihood based training on biased trajectory data with subsequent fine-tuning using energy-based training (as done in parallel WaveNet). Such models allow rapid exploration using MC exploration in latent space and computing free energy differences between disconnected states.

Paper: https://science.sciencemag.org/content/365/6457/eaaw1147

Editorial putting the paper into context: https://science.sciencemag.org/content/365/6457/982