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Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations...

by Junghoon Chae, Debsindhu Bhowmik, Heng Ma, Arvind Ramanathan, Chad A Steed
Publication Type
Conference Paper
Book Title
2019 IEEE International Conference on Big Data (Big Data)
Publication Date
Page Numbers
1759 to 1764
Publisher Location
New York, United States of America
Conference Name
IEEE International Conference on Big Data
Conference Location
Los Angeles, California, United States of America
Conference Sponsor
Conference Date

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).