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Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders
[article]
2019
arXiv
pre-print
Extracting insight from the enormous quantity of data generated from molecular simulations requires the identification of a small number of collective variables whose corresponding low-dimensional free-energy landscape retains the essential features of the underlying system. Data-driven techniques provide a systematic route to constructing this landscape, without the need for extensive a priori intuition into the relevant driving forces. In particular, autoencoders are powerful tools for
arXiv:1912.12175v1
fatcat:uzy3zhhafbfwrhs5skr4o57uxy