Equivariant Flows: sampling configurations for multi-body systems with symmetric energies [article]

Jonas Köhler, Leon Klein, Frank Noé
2019 arXiv   pre-print
Flows are exact-likelihood generative neural networks that transform samples from a simple prior distribution to the samples of the probability distribution of interest. Boltzmann Generators (BG) combine flows and statistical mechanics to sample equilibrium states of strongly interacting many-body systems such as proteins with 1000 atoms. In order to scale and generalize these results, it is essential that the natural symmetries of the probability density - in physics defined by the invariances
more » ... of the energy function - are built into the flow. Here we develop theoretical tools for constructing such equivariant flows and demonstrate that a BG that is equivariant with respect to rotations and particle permutations can generalize to sampling nontrivially new configurations where a nonequivariant BG cannot.
arXiv:1910.00753v1 fatcat:4tylnzbyl5dhbeshha5mxr7ade