Electronic Structure Models Using Coarse-Grained Representations

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Professor Nicholas Jackson of the University of Illinois, Urbana-Champaign, is supported by an award from the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry for research to develop computer simulation methods to support the discovery and development of complex molecular and materials systems. Targeted research areas include the design of new materials for use in low-cost batteries, solar cells, and organic microelectronics. This theoretical and computational research will advance science by improving our fundamental understanding of how to design systems that better harvest, store, and control energy. While modeling of smaller systems has significantly progressed in recent years, characterizing, understanding, and controlling the complex behavior of large collections of atoms and molecules is hindered by slow and computationally costly simulation methods. Nicholas Jackson and his group will develop new computational methods that will dramatically increase the speed, and decrease the cost, of performing computer simulations of such technologically important systems. This work will train a new generation of scientists fluent in both chemistry and advanced computer simulation methods, including data science and machine learning. It will also set the foundation for a new curriculum that will introduce machine learning and data science methods to Chemistry domain scientists.Professor Nicholas Jackson and his group will establish a new paradigm for scalable quantum chemical predictions utilizing electronic prediction models that act on coarse-grained molecular representations. They will develop methods via systematic parameterization against underlying quantum mechanical datasets, in analogy with the existing array of coarse-grained modeling methods of molecular dynamics that are parameterized against underlying all-atom force-fields. Specifically, the new methods will deal with (1) electronic prediction models by renormalizing all-atom quantum chemistry predictions to coarse-grained representations and (2) dimensionality reduction techniques to identify electronically active collective variables for mapping operator identification and model Hamiltonian design. The new developments are expected to have a similar impact on condensed phase electronic predictions as molecular software has had on single molecule predictions in the molecular sciences, transforming the design of condensed phase materials systems across chemical applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.