CAREER: Integrating Grassmannians with Electronic Structure Theory for Next-Generation Quantum Chemistry

Professor Ka Un Lao of Virginia Commonwealth University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop innovative methods for simulating complex systems, such as transition metal-containing catalysts used in energy conversion technologies. Understanding how these systems interact with light is crucial for designing more efficient catalysts and advancing clean energy solutions. Current computational tools often fail to simulate these systems due to instability and divergence issues. To address this, the Lao research group will develop a new theoretical framework combing advanced quantum chemistry techniques with mathematical tools from differential geometry. This framework will enable stable and accurate long-term simulations feasible, providing valuable insights into the photodynamic mechanisms of molecular solar thermal systems and accelerating the design of efficient clean energy storage materials. The computational tools developed will be made available through widely used software, benefiting the scientific community. In addition to the research, Professor Lao will modernize chemistry education by incorporating Python programming into curricula, equipping students with essential skills for the data-driven future of science. Professor Lao will also mentor a diverse group of students from high school through graduate levels, inspiring future scientists and promoting inclusivity in STEM. Under this award, Professor Ka Un Lao of Virginia Commonwealth University will develop a next-generation theoretical framework for accurate, efficient, and stable quantum chemistry simulations, addressing longstanding computational challenges in transition-metal-containing systems. Current methods for ab initio molecular dynamics (AIMD) often fail due to self-consistent field (SCF) convergence issues, limiting their applicability for long-timescale simulations of complex systems. To overcome these limitations, the Lao research group will integrate Grassmannians from differential geometry with advanced electronic structure methods and a novel SCF solver. This Grassmannian-augmented framework will enable stable AIMD simulations, pushing the boundaries of quantum chemistry into previously intractable systems. The Lao research group will focus on elucidating the photodynamic mechanisms of molecular solar thermal systems, facilitating the pre-synthesis screening and design of efficient clean energy storage materials. Computational tools developed during this project will be integrated into widely used quantum chemistry software, ensuring accessibility to the broader scientific community. Additionally, the project emphasizes education and outreach by incorporating Python programming into undergraduate and graduate chemistry courses, fostering computational literacy. Training opportunities will be provided to students in advanced quantum chemistry, equipping them for successful careers in STEM. 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.