DELTA-Topology: A Science Gateway for Experimental and Computational Chemical Data Analysis using Topological Models

Abstract

Chemical data are diverse and complex, spanning point cloud data and manifolds, and occurring with potentially large dimensions. They are obtained from experimental and computational modeling, and may encode complex correlations of particle/molecular configurations and dynamic motion. It is a significant challenge to identify such correlations, reduce dimensionality, and identify the shapes and topologies of both point cloud data and chemistry-derived surfaces (e.g., energy landscapes of chemical transformation). Chemical graph theory and computational topology offer powerful new tools for the chemistry community, however, dissemination and implementation of the tools’ associated algorithms and methods has been hampered by a lack of supporting infrastructure. In this manuscript, we describe the DELTA Science Gateway, which integrates several types of mathematical and topological analysis software for chemical data analysis. The focus is on energy landscape data derived from experimental and computational modeling techniques in order to understand the principles involved in structure and function. The DELTA gateway is hosted under the SciGaP project at Indiana University and is powered by Apache Airavata gateway middleware framework. The gateway provides an integrated infrastructure for simulations and analysis on XSEDE resources, as well as interactive access through a VNC client and a JupyterHub deployed on the Jetstream cloud using virtual clusters.