Research Topics

Correlated Excited States and Competing Photophysical Processes

The Röhr group studies light-induced processes in electronically coupled molecular systems, where correlated excited states give rise to competing pathways such as energy transfer, charge separation, excimer formation, singlet fission, and triplet–triplet annihilation. These phenomena arise from the interplay of local excitations, charge-transfer states, and multiexcitonic configurations whose couplings are highly sensitive to molecular packing and chemical composition. Beyond photophysics, we investigate how correlated and entangled electronic states can be mapped onto effective low-energy Hamiltonians, providing a foundation for optically addressable molecular spin states and organic qubit architectures.

SymbolicCI: Analytic Model Hamiltonians for Multiexcitonic Systems

A central focus of the group is the development of Symbolic Configuration Interaction (SymbolicCI), an analytic, fragment-based electronic-structure methodology for molecular aggregates of extended size. SymbolicCI constructs spin-adapted many-electron Hamiltonians in second quantization using symbolic operator algebra and Jordan–Wigner mappings, enabling the explicit inclusion of multiexcitonic and spin-correlated states beyond the dimer limit. The resulting analytic Hamiltonians provide chemically transparent access to electronic couplings and serve as a basis for structure–function sampling, rate modeling, and efficient quantum and quantum–classical dynamics simulations (DFG Research Grant, Project No. 555181242).

Photochemistry in Complex and Confined Media

Many functional photochemical processes occur in complex environments such as supramolecular matrices, solid-state assemblies, and biological scaffolds. We investigate how environmental confinement and heteroatom substitution reshape excited-state landscapes and redirect competing pathways, combining multireference electronic-structure theory with quantum–classical dynamics simulations. Current work includes photophysics in supramolecular matrices (IRTG 2991) and photochemistry of boron-containing chromophores and photoswitches (CRC 1762).  

Inverse Design and Machine Learning for Photofunctional Materials

We develop inverse design strategies that integrate analytic electronic-structure models, enhanced sampling, and machine learning to identify molecular and supramolecular architectures with targeted photophysical behavior. Central to this effort is the derivation of physically motivated electronic-structure descriptors from quantum chemistry, enabling an interpretable description of structure–function relationships. Building on these descriptors, we employ supervised learning and diffusion-based generative models, in close collaboration with experimental partners, including applications to fluorescence activation in RNA aptamer–chromophore complexes and the generative design of photofunctional aggregates.