SHNITSEL Contributors




Theodor Röhrkasten
Theodor developed the shnitsel.dynamic module of the shnitsel Python package, which handles the parsing and loading of datasets, post-processing of static and dynamic data, and visualization of high-dimensional trajectory data. His work ensures that shnitsel can efficiently analyze and represent complex molecular dynamics simulations. Additionally, he collaborated on the development of the shnitsel website.


Robin Curth
Robin developed the shnitsel.static module of the shnitsel Python package, which provides specialized visualization tools for static data. His contributions enable clear and effective representation of key molecular properties and simulation results.


Carolin Müller
Carolin co-conceptualized and supervised the shnitsel project, overseeing its development and integration into computational workflows. She developed, illustrated and maintains the shnitsel website.
carolin.cpc.mueller@fau.de


Julia Westermayr
Julia co-conceptualized and supervised the shnitsel project, ensuring its development aligned with the needs of the scientific community. She co-organized the CECAM workshop where the idea for shnitsel was initiated.
julia.westermayr@uni-leipzig.de

Team, Vienna 2023
The SHNITSEL initiative originated initiative originated at the CECAM workshop Machine-learned potentials in molecular simulation: best practices and tutorials (1211), co-organized by J. Westermayr and C. Oostenbrink in Vienna in 2023. It emerged from discussions within the surface hopping team:

    Brigitta Bachmair (University of Vienna) Rachel Crespo-Otero (University College London) Steven Lopez (Northeastern University) Sascha Mausenberger (University of Vienna) Carolin Müller (University of Luxembourg) Max Pinheiro Jr (Aix-Marseille Université) Štěpán Sršeň (University of Vienna) Julia Maria Westermayr (Leipzig University) Graham Worth (University College London)




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