Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot," vast computational effort is invested for simulating these systems in small steps, e.g., using molecular dynamics. Combining deep learning and statistical mechanics, we developed …