Research

Welcome to our group, where we explore light-induced phenomena in molecules and materials. Our main focus centers on unraveling mechanisms through the synergy of quantum chemistry and chemoinformatics complemented by spectroscopy. Our investigation spans captivating phenomena such as:

• photocatalysis
• photoswitching
• photodecaging

We explore the captivating realm of light-induced processes in chemistry, from electron transfers to isomerization. Our interdisciplinary approach merges quantum chemistry, chemoinformatics, and spectroscopy. By decoding molecular reactions and pathways using advanced computational methods, we strive to unlock the secrets of photoinduced phenomena. Our mission is to guide the optimization of light-driven processes, achieving enhanced reactivity and efficiency.

	Quantum PhotoAlchemy We are a group that combines the fields of quantum chemistry and machine learning to explore the mechanisms behind photochemical reactions. Our aim is to understand the unique reactivities and selectivities that arise upon photoexcitation of molecules. We use TD-DFT and MCSCF calculations, as well as non-adiabatic molecular dynamics simulations, to investigate these mechanisms. Our work involves independent research and collaborations with partners worldwide.
	Unraveling Mechanisms In our research group, we are dedicated to tackling the intricate challenges that arise when dealing with mechanistic modeling of multivariate data, especially those exceeding three dimensions. The underlying complexities can often obscure the mechanisms at play. Our mission is clear: to unravel this complexity. We focus on extracting invaluable mechanistic insights from multivariate spectroscopic data, with a specific emphasis on time-resolved spectra. To achieve this, we harness the power of chemometric and machine learning techniques. Through methods like multivariate curve resolution, principal component analysis, and neural networks, we aim to decode the underlying mechanisms.
	Spot-On Design Our group specializes in the challenging realm of predicting photoinduced phenomena: While machine learning architectures achieve accurate predictions, building vast chemical databases from quantum chemical reference calculations poses limitations. Our mission is to transfer knowledge gained from simulations into processing experimental data, and vice versa, enabling us to systematically calculate relevant structures. Through automated knowledge transfer between quantum chemistry and multivariate modeling of experimental data, we develop fast workflows for understanding and optimizing excited state properties. Our goal is to construct comprehensive databases and data-driven strategies for designing molecules and materials with desired excited state properties, such as optical characteristics.