(2jp) Multi-Level Simulation Driven Discovery of Correlated Materials for Carbon Capture, Biomimetic Catalysis, and Quantum Information Science

Atomic-level quantum chemical simulation is poised to play a key role in advancing developments in the fields of quantum information science (QIS), carbon capture and storage (CCS) technologies, and metalloenzymatic or cluster-based catalysis by elucidating the fundamental factors governing complex physical and electronic structures in strongly correlated molecular and extended materials. We are particularly interested in resolving the fundamental electronic and magnetic interactions in systems that present targets in the development of qubits, spintronic devices, and biomimetic metalloclusters and metalloenzymes, as well as the atomic-level processes of promising CO₂ capture technologies, particularly via carbonate mineralization, or metal-organic-framework and ionic-liquid based absorption. Here, the accurate simulation of experimentally relevant and measurable characteristics requires the precise resolution of effects arising from electron correlation and structural dynamics, which provides a formidable challenge to traditional electronic structure methods. We propose the development low-scaling reduced-density-matrix (RDM) based algorithms for the resolution of all-electron correlation and their utilization within novel machine-learning accelerated multi-level embedding schemes. Exploiting the reduced scaling that RDM and embedding theories offer over wavefunction based algorithms will allow us to build a suite of tools that enables the accurate simulation of highly orbitally degenerate systems and facilitates the description of processes occurring in the solid state, solution or at interfaces.

Teaching Interests

My teaching philosophy is rooted in a commitment to ensure that every student has the same opportunity to succeed, specifically recognizing that students come to both the classroom and research from diverse educational and personal backgrounds. As an educator, I aim to provide students personal mentorship with a focus on inclusivity. By fostering an approachable and supportive environment, and providing individualized support, I strive to enable the success of all students, regardless of their background or prior educational experiences.

As a theoretical chemist by training, I particularly enjoy teaching the fundamental theoretical concepts of a wide area of chemistry, physics, and engineering science to undergraduate and graduate students of diverse tracks. This includes teaching both fundamental, as well as advanced topics in physical and computational chemistry, quantum mechanics, and solid-state physics, including courses on computer simulation techniques, thermodynamics, statistical mechanics, and kinetics. Placing an emphasis on the use of contemporary computational methods, I plan to incorporate interactive examples based on modern computational tools such as Jupyter Notebooks into my curriculum. I believe that this provides a highly valuable pedagogical tool in teaching abstract theoretical and mathematical concepts and allows us to equip students with computational problem-solving skills that are becoming increasingly important for not only for theoretical but also experimental researchers in physical and engineering sciences, as well as in STEM and non-STEM careers.