Embedded-physics machine learning for coarse-graining and collective variable discovery without data

24 Feb 2020Markus SchöberlNicholas ZabarasPhaedon-Stelios Koutsourelakis

We present a novel learning framework that consistently embeds underlying physics while bypassing a significant drawback of most modern, data-driven coarse-grained approaches in the context of molecular dynamics (MD), i.e., the availability of big data. The generation of a sufficiently large training dataset poses a computationally demanding task, while complete coverage of the atomistic configuration space is not guaranteed... (read more)

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