Hier finden Sie einen Überblcik über unsere wichtigsten Publikationen, Vorträge und Abschlussarbeiten.
Ausgewählte Publikationen
J. Westermayr,* J. Gilkes, R. Barrett, R. J. Maurer,*
High-throughput property-driven generative design of functional organic molecules
Nat. Comput. Sci. 3, 139-148 (2023), DOI
J. Westermayr, M. Gastegger, D. Vörös, L. Panzenboeck, F. Joerg, L. González, P. Marquetand#
Deep learning study of tyrosine reveals that roaming can lead to photodamage
Nat. Chem. 14, 914-919 (2022), DOI
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. Phys. Chem. Lett. 13 (17), 3812-3818 (2022), DOI
J. Westermayr, P. Marquetand
Machine learning for electronically excited states of molecules
Chem. Rev. 121, 16, 9873-9926 (2021), DOI
Unsere neuesten Vorabdrucke
J. Westermayr,* J. Gilkes, R. Barrett, R. J. Maurer,*
High-throughput property-driven generative design of functional organic molecules
arXiv:2207.01476 (2022), DOI