Data Science + Topology = Chemistry
The project “Descriptors of Energy Landscapes Using Topological Data Analysis (DELTA)” is an National Science Foundation (NSF) funded HDR Institute Frameworks that is advancing topological data analysis (TDA) and machine learning algorithms for the study of intensive and complex data sets of the energy landscapes (EL) of chemical systems. TDA as an innovative approach to solve long-standing challenges in Chemistry, that include but are not limited to "the real-time optimization and control of complex chemical systems." Fundamentally these challenges derive from an unsophisticated understanding of the EL of a chemical system, which dictates the outcomes of all chemical transformation. Chemists generally do not know how the EL changes as a function of system conditions, nor are there quantifiable relationships between intra- and intermolecular interactions and EL topological features. The EL contains much more information than has been interpreted and TDA has the potential to extract new knowledge that fundamentally changes research paradigms. In allegory to the machine learning field a decade ago, fundamental research is needed to learn how to adapt TDA for chemistry applications and new tools must be developed that are accessible to domain experts.
This project is part of the National Science Foundation's Harnessing the Data Revolution Big Idea activity. The effort is jointly funded by the Division of Chemistry within the NSF Directorate for Mathematical and Physical Sciences.