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arXiv · 1910.00753

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

Jonas Köhler·Leon Klein·Frank Noé
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Abstract

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 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.

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BibTeXRIS

Jonas Köhler, Leon Klein, Frank Noé. 2019-10-02. Equivariant Flows: sampling configurations for multi-body systems with symmetric energies. https://arxiv.org/abs/1910.00753

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