Dresden 2020 – scientific programme
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DY: Fachverband Dynamik und Statistische Physik
DY 40: Data Analytics, Extreme Events, and Nonlinear Stochastic Systems (joint session DY/SOE)
DY 40.3: Talk
Wednesday, March 18, 2020, 15:45–16:00, ZEU 118
Interpretable Embeddings from Molecular Simulations Using Gaussian Mixture Variational Autoencoders — •Yasemin Bozkurt Varolgunes1, 2, Tristan Bereau1, and Joseph F. Rudzinski1 — 1Max Planck Institute for Polymer Research, Mainz, Germany — 2Koc University, Istanbul, Turkey
Extracting insight from the molecular simulations data requires the identification of a few collective variables (CVs) whose corresponding low-dimensional free-energy landscape (FEL) retains the essential features of the underlying system. Autoencoders are powerful tools for dimensionality reduction, as they naturally force an information bottleneck. While variational autoencoders (VAEs) ensure continuity of the embedding by assuming a Gaussian prior, this is at odds with the multi-basin FELs that typically arise from the identification of meaningful CVs. Here, we incorporate this physical intuition into the prior by employing a Gaussian mixture variational autoencoder (GMVAE), which encourages the separation of metastable states within the embedding. The GMVAE performs dimensionality reduction and clustering within a single unified framework, and is capable of identifying the inherent dimensionality of the input data, in terms of the number of Gaussians required to categorize the data. We illustrate our approach on two toy models and a peptide, demonstrating the anti-clustering effect of the prior relative to standard VAEs. The resulting embeddings stand as appropriate representations for constructing Markov state models, highlighting the transferability of the dimensionality reduction from static equilibrium properties to dynamics.