Julia Westermayr University of Vienna Institute of Theoretical Chemistry Währinger Str. 17 1090 Vienna Austria julia.westermayr@univie.ac.at +43 1 4277 52766 |
Focus Area |
The focus of my work is the development of artificial neural network potentials and their implementation into our own ab-initio molecular dynamics program SHARC (Surface Hopping including ARbitrary Couplings). By running molecular dynamics with the SHARC program, we are currently investigating the first step of the underlying mechanism of dityrosine crosslinking. |
Awards & Prizes |
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News |
Publications |
Focus on Learning Excited-state Properties in General Learning Excited-state Properties. In Quantum Chemistry in the Age of Machine Learning. Chapter 20 , 467-488, (2023), DOI: 10.1016/B978-0-323-90049-2.00004-4 12. K. Cseh, H. Geisler, K. Stanojkovska, J. Westermayr, P. Brunmayr, D. Wenisch, N. Gajic, M. Hejl, M. Schaier, G. Koellensperger, M. Jakupec, P. Marquetand, W. Kandioller Arene Variation of Highly Cytotoxic Tridentate Naphthoquinone-Based Ruthenium (II) Complexes and In-Depth In Vitro Studies Pharmaceutics 14, 2466, (2022), DOI: 10.3390/pharmaceutics14112466 11. J. Westermayr, M. Gastegger, D. Vörös, L. Panzenböck, F. Jörg, L. González, P. Marquetand Deep Learning Study of Tyrosine Reveals that Roaming can Lead to Photodamage Nat. Chem. 14 , 914–919, (2022), DOI: 10.1038/s41557-022-00950-z 10. B. Lier, P. Poliak, P. Marquetand, J. Westermayr, C. Oostenbrink BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network/Molecular Mechanics Simulations J. Chem. Phys. 13, 3812–3818, (2022), DOI: 10.1021/acs.jpclett.2c00654 B. Lier, P. Poliak, P. Marquetand, J. Westermayr, C. Oostenbrink Tridentate 3-Substituted Naphthoquinone Ruthenium Arene Complexes: Synthesis, Characterization, Aqueous Behavior, and Theoretical and Biological Studies Inorg. Chem. 60, 9805–9819, (2021), DOI: 10.1021/acs.inorgchem.1c01083 8. J. Westermayr, P. Marquetand Machine learning for electronically excited states of molecules Chem. Rev. 121, 9873-9926, (2021), DOI: 10.1021/acs.chemrev.0c00749 J. Westermayr, P. Marquetand Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space J. Chem. Phys. 153, 154112, (2020), DOI: 10.1063/5.0021915 J. Westermayr, P. Marquetand Machine learning and excited-state molecular dynamics Mach. Learn.: Sci. Technol. 1, 043001, (2020), DOI: 10.1088/2632-2153/ab9c3e J. Westermayr, P. Marquetand Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics J. Phys. Chem. Lett. 11, 3828-3834, (2020), DOI: 10.1021/acs.jpclett.0c00527 J. Westermayr, M. Gastegger, P. Marquetand Neural networks and kernel ridge regression for excited states dynamics of CH2NH+2: From single-state to multi-state representations and multi-property machine learning models Mach. Learn.: Sci. Technol. 1, 025009, (2020), DOI: 10.1088/2632-2153/ab88d0 J. Westermayr, F. A. Faber, A. S. Christensen, O. A. von Lilienfeld, P. Marquetand Machine Learning for Nonadiabatic Molecular Dynamics In: Hugh Cartwright (ed) Machine Learning in Chemistry: The Impact of Artificial Intelligence Theoretical and Computational Chemistry Series, Royal Society of Chemistry , Chapter 4, (2020), DOI: 10.1039/9781839160233-00076 2. J. Westermayr, M. Gastegger, M. Menger, S. Mai, L. González, P. Marquetand Machine Learning Enables Long Time Scale Molecular Photodynamics Simulations Chem. Sci. 10, 8100-8107, (2019), DOI: 10.1039/C9SC01742A Cover Image: J. Westermayr, M. Gastegger, M. Menger, S. Mai, L. González, P. Marquetand |