Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational Autoencoders

22 Dec 2019Yasemin Bozkurt VarolgunesTristan BereauJoseph F. Rudzinski

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... (read more)

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