Nathan Pierrat

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Nathan navigates the landscape of photoinduced electron transfer processes in dyads composed of perylene chromophores and Rh catalytic centers.

• Projects: Photoredoxcatalysis

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Pub Rutzoh

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From Two to More

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Martina officially became a Ph.D. student in the CPC group today! Her research will focus on exploring the dynamic 'dance' of photomotors and photoswitches using non-adiabatic molecular dynamics simulations. 🌟💡 With her arrival, our group has expanded, transitioning from a duo into a collaborative group.

#NewPhD #NAMD #Photoswitches

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Martina Hartinger

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Martina is a chemist exploring the dynamic world of molecular photomotors and -switches by means of non-adiabatic molecular dynamics simulations.

• Projects: Photomotors & -switches

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Theodor joins Group

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Theodor is now part of the CPC group, where he will delve into the fascinating field of excited state properties, starting from a precursor and moving towards understanding 2D materials. 🧱🌟💡 #NewPhD #ComputationalMaterialsScience

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Formation of the Group

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Today I started my position as tenure track junior professor for the theory of electronically excited states at FAU Erlangen-Nuremberg! It has been an exciting journey through academia, starting with a PhD at the Friedrich Schiller University Jena and the Leibniz Institute for Photonic Technologies with Benjamin Dietzek-Ivansic, followed by a PostDoc period in Jena and as a Feodor Lynen Researcher of the Alexander von Humboldt Foundation at the University of Luxembourg with Alexandre Tkatchenko, and now the creation of my own group.
I am super grateful for this fantastic opportunity to pursue my own research at the age of just 28! A big thank you goes out to all the people who have supported and encouraged me and with whom I have collaborated over the years! Without you, this would not have been possible!

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Theodor Everley Röhrkasten

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Theo is a chemist with an extraordinary talent for languages. He's fluent in English, German, French, and Japanese, as well as in programming languages like LaTeX, Python, Bash, and C++.

• Projects: Computational Materials Science

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Photochemical Reactions – Quantum Chemistry Meets Machine Learning

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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.

• Team: n.a.
• Research Field: Quantum Chemistry, Machine Learning
• Projects: n.a.

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Mechanism of photochemical reactions and photophysical processes

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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.

• Team: n.a.
• Research Field: Data Science
• Projects: n.a.

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Target and Inverse Design of Excited State Properties

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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.

• Team: n.a.
• Research Field: Machine Learning
• Projects: n.a.

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Pub Goger Pccp

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Mode

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Lab

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Team Spirit.

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yISPPCC

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Carolin at ySPPCC 2023.

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Pub Mueller Jpca

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