Publications

Recent Highlights

  • From Excited-State Dynamics to Photoproduct Formation

    Excited-state simulations explain why related aza-diarylethenes form different photoproducts upon irradiation.
  • SHNITSEL-tools

    No more tedious scripting or fragmented analysis! With shnitsel-tools, you can automate filtering, visualize dynamics, and uncover excited-state mechanisms across multiple molecules.
  • ChromoPredict

    What happens when the wisdom of mid-20th-century chemists meets today’s digital tools?
  • Multistate Modeling of Hemithioindigo Isomerization

    We present a multistate molecular mechanics model that captures both the ground (S0) and triplet excited (T1) states of hemithioindigo-based photoswitches — enabling nanosecond-scale molecular dynamics with near quantum mechanical accuracy.
  • Machine Learning for Non-adiabatic Molecular Dynamics

    Curious about how machine learning is transforming excited-state molecular dynamics?
  • SHNITSEL-data

    SHNITSEL-data (Surface Hopping Nested Instances Training Set for Excited-state Learning) is an open-access dataset containing 418,870 high-accuracy ab initio data points for nine organic molecules. It includes quantum chemical properties in ground and electronically excited singlet and triplet states, such as energies, forces, dipole moments, nonadiabatic couplings, transition dipoles, and spin-orbit couplings. Generated with state-of-the-art […]
  • From Triplet to Twist

    rundes Bild mit Kurven und organischen MolekülenHow does Near-Infrared-Photoswitching work? Our newest research delivers an ultrafast molecular movie of the all-red-light photoswitch peri-Anthracenethioindigo (PAT) in action! Guided by excited-state simulations, its mechanism of motion is fully revealed. We show that PAT double bond rotation occurs exclusively from the triplet state – but it is stable in air due to very favorable […]
  • SPaiNN for machine learning driven excited-state dynamics

    Meet SPaiNN, our open-source Python toolkit for machine learning driven excited-state dynamics! By combining equivariant neural networks (from SchNetPack) with SHARC’s surface hopping engine, SPaiNN makes non-adiabatic molecular dynamics faster and more accessible — without sacrificing quantum accuracy. Equivariant models shine in our tests on methyleneimmonium and alkene systems, outperforming invariant ones in accuracy and […]

List of Publications

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2020

2019

2017

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