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[R] Deep generative networks allow for efficient generation of samples from the Boltzmann distribution of complex multi-body systems.

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

submitted by /u/konasj
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.