Multiconfiguration Nonclassical-Energy Functional Theory For Spectroscopy and Dynamics

University Of Chicago, Chicago IL

Investigators

Abstract

Laura Gagliardi of the University of Chicago and Donald G. Truhlar of the University of Minnesota are supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop electronic structure theories for studying strongly correlated systems relevant to energy growth and sustainability. Gagliardi and Truhlar will develop advanced methods to enhance the understanding of catalysis, photochemistry, spectroscopy, materials science, and atmospheric chemistry. Their work aims to explain phenomena observed by experimentalists and predict new chemical behaviors in these fields. The proposed research targets unprecedented levels of accuracy and applicability for quantum chemical methods. Additionally, they will continue to develop and improve open-source computational codes, making them freely available to the scientific community with extensive documentation. The collaboration between these two teams will also provide students and postdoctoral researchers with a broad range of ideas and experiences, fostering well-rounded career development. Gagliardi and Truhlar will focus on developing multiconfiguration nonclassical-energy functional theory (MC-NEFT), optimizing its functional components by creating databases for multiconfiguration systems and training the method against these datasets to improve both accuracy and scope. They will also develop automated procedures for selecting active spaces, streamlining calculations for excited states and reactivity studies while reducing reliance on human intuition. Additionally, they will enhance methods for using MC-NEFT to model excited-state dynamics in systems involved in photo-induced processes, such as light-harvesting, photocatalysis, photosensing, vision, and DNA photostability. This density-functional-based approach will enable quantitative modeling of larger systems over longer time scales, incorporating extensive ensemble averaging for greater predictive power. 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.