Embedded Files
THEORY
Reduced-Density-Matrix Methods
Reduced-Density-Matrix Methods
Exploiting the fact that electrons interact pairwise, one can express the electronic energy as a functional of the 1- and 2-RDMs, allowing for the calculation of the electronic energy without the N-electron wavefunction. We aim to develop 1- and 2-RDM based algorithms to overcome scaling limitations of traditional wavefunction based algorithms.
Multi-Level Embedding Methods
Multi-Level Embedding Methods
Chemistry occurs in solution, the solid state and at interfaces! Utilizing multi-level embedding methods allows us to bridge different length- and time-scales in the accurate description of electron correlation. We capture long-range structural effects and dynamics using lower-cost methods, such as DFT or by employing ML, while the electronic structure of the most important sub-unit is calculated using a high-level embedded correlated wavefunction or RDM theory.
SIMULATION
Quantum Materials and Molecules for Quantum Information Science
Quantum Materials and Molecules for Quantum Information Science
Utilizing RDM methods we aim to elucidate the electronic, magnetic, and photophysical properties of strongly correlated multi-nuclear complexes. We are particularly interested in modeling compounds that are targets in the development of single-molecule magnets, qubits, NIR emitters, and photocatalysts.
Carbon Capture & Storage
Carbon Capture & Storage
Carbon capture and storage (CCS) technologies remain in their infancy as challenges are faced in the ab- or ad-sorption of carbon dioxide, and its long-term storage. Utilizing multi-level embedding simulations, we aim to characterize the electronic and structural factors driving the mineralization of carbon dioxide, as well as its absorption and chemisorption in metal-organic frameworks and ionic liquids.
Metalloenzymes & Metalloclusters
Metalloenzymes & Metalloclusters
Artificial metalloenzymes promise to provide highly active, selective, and sustainable catalytic pathways; however, significant knowledge gaps remain due to the large number of metal centers comprising metal-cofactors such as [4Fe-4S], [8Fe-7S], or FeMoco, giving rise to strongly correlated electronic structures, while the importance of coordination sphere effects and structural dynamics adds additional complexity. Utilizing RDM and multi-level embedding approaches, we aim to understand the role of metal cofactors’ electronic structure throughout the catalytic cycle while simultaneously capturing the role of coordination sphere effects and structural fluxionality.
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