Quantum mechanics and machine learning
for sustainable reactions and materials

Group Updates:

Research Areas

Mechanistic Organic photochemistry

Our group uses quantum chemistry and machine learning to understand the origin of reactivities and selectivities of organic photochemical reactions. We leverage multiconfigurational calculations (CASSCF) calculations and non-adiabatic molecular dynamics independently and with collaborators across the world. We developed the machine learning techniques, pyRAIMD to accelerate NAMD simulations by 100,000x by accelerating predictions of energies, gradients, and non-adiabatic coupling reactions.

High-throughput screening and VERDE materials DB

Our group has developed an automated workflow that performs quantum mechanical calculations on 1000s of organic chromophores with applications in catalysis, photomedicine, and solar energy harvesting. The results are made available online via the Virtual Excited State Reference for the Discovery of Electronic materials database (VERDE materials DB;

Solid state modeling of organic photovoltaics

Organic photovoltaics (OPVs) are an increasingly efficient type of sustainable solar cell. We develop next-generation co-crystalline OPVs based on supramolecular design principles. The unique shapes of contorted π- conjugated materials promotes non-covalent interactions, facilitates charge separation, and improves efficiencies in OPVs. Current interests involve non-adiabatic simulations of charge transfer dynamics.