Extensive deep neural networks for transferring small scale learning to large scale systems

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<p>We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with <graphic xmlns:xlink="http://www.w3.org/1999/xlink" id="ugt1" xlink:href="http://pubs.rsc.org/SC/2019/c8sc04578j/c8sc04578j-t1..gif" /> scaling.</p>

Journal

  • Chemical Science

    Chemical Science 10 (15), 4129-4140, 2019

    Royal Society of Chemistry (RSC)

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