The Lopez Research Group: Computational Photochemistry
ML-accelerated photodynamics
Multiconfigurational quantum mechanical calculations are typically needed to describe the electronic structures of molecular excited states and conical intersections. We leverage our open-access machine learning code (PyRAI2MD) to provide unprecedented mechanistic understanding via substantial acceleration in photodynamics simulations, especially with large molecules and long timescales.
Big-data discoveries of chromophores
The Lopez group uses computational and machine-learning techniques to explore the vast chemical space of organic chromophores. Quantum mechanical calculations provide the electronic structure and properties; machine learning techniques help to identify promising candidates and accelerate the predictions of molecular properties. Lopez created the Virtual Excited State Reference for the Discovery of Electronic Materials database (VERDE materials DB) to share these results with the world.
Materials Chemistry
The Lopez group is highly interested in identifying new materials capable of converting solar energy into electricity through organic photovoltaics. We have published several papers focused on developing new covalent organic frameworks capable of simultaneously maximizing long-rage order and donor-acceptor surface areas for next-generation photovoltaics.
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