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SHNITSEL
Surface Hopping Nested Instances Training Set for Excited-state Learning

– Excited States – Photochemical Reactions – Photodynamics –

SHNITSEL Molecules


About


SHNITSEL is a centralized platform compiling surface hopping datasets relevant for photochemical and photophysical studies. The database currently provides information on key photochemical processes, including photodissociation, roaming mechanisms, E/Z-isomerization, and electrocyclic reactions. These processes are fundamental in various applications, from synthetic organic chemistry to the development of new theoretical and machine learning (ML)-based methods.

The dataset consists of extensive ab initio calculations covering ground and excited-state properties, including energies, forces, dipole moments, nonadiabatic couplings, transition dipoles, and spin-orbit couplings. These high-accuracy quantum chemical properties serve as a valuable benchmark for validating computational approaches and training ML models for excited-state dynamics.


SHNITSEL is intended as a growing resource for the community. Researchers are encouraged to contribute by sharing links to their own datasets, helping expand the database’s scope. If you would like to add your dataset, feel free to reach out!





Related Works




A01-A03

S. Mausenberger, C. Müller, A. Tkatchenko, P. Marquetand, L. Gonzalez, J. Westermayr
SpaiNN: equivariant message passing for excited-state nonadiabatic molecular dynamics
Chem. Sci. 2024 , DOI: 10.1039/D4SC04164J



T01

J. Westermayr, M. Gastegger, D. Vörös et al.
Deep learning study of tyrosine reveals that roaming can lead to photodamage.
Nat. Chem. 2022 , 14 , 914-919 , DOI: 10.1038/s41557-022-00950-z



R02

I. Polyak, L. Hutton, R. Crespo-Otero, M. Barbatti, P. J. Knowles
Ultrafast Photoinduced Dynamics of 1,3-Cyclohexadiene Using XMS-CASPT2 Surface Hopping
Journal of Chemical Theory and Computation 2019 , 15  (7) , 3929-3940 , DOI: 10.1021/acs.jctc.9b00396



I01

J. Westermayr, M. Gastegger, M. Menger, S. Mai, L. González, P. Marquetand
Machine learning enables long time scale molecular photodynamics simulations
Chem. Sci. 2019 , 150 , 8100-8107 , DOI: 10.1039/C9SC01742



H01

S. Horton, Y. Liu, R. Forbes, V. Makhija, R. Lausten, A. Stolow, P. Hockett, P. Marquetand, T. Rozgonyi, T. Weinacht
Excited state dynamics of CH2I2 and CH2BrI studied with UV pump VUV probe photoelectron spectroscopy
The Journal of Chemical Physics 2019 , 150 , 174201 , DOI: 10.1063/1.5086665