Hier finden Sie einen Überblcik über unsere wichtigsten Publikationen, Vorträge und Abschlussarbeiten.
Ausgewählte Publikationen
- S. Mausenberger, C. Müller, A. Tkatchenko, P. Marquetand, L. González, J. Westermayr
SpaiNN: equivariant message passing for excited-state nonadiabatic molecular dynamics.
Chem. Sci. 2024, 15, 15880. DOI - R. Barrett, J. Westermayr
Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of Molecules.
J. Phys. Chem. Lett. 2024, 15, 349. DOI - T. Oestereich, R. Tonner-Zech, J. Westermayr
Decoding energy decomposition analysis: Machine-learned Insights on the impact of the density functional on the bonding analysis.
J. Comput. Chem. 2024, 45, 368. DOI - J. Westermayr, J. Gilkes, R. Barrett, R. J. Maurer
High-throughput property-driven generative design of functional organic molecules.
Nat. Comput. Sci. 2023, 3, 139. 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. 2022, 14, 914. DOI
Unsere neuesten Vorabdrucke
- R. Barrett, J. C. Dietschreit, J. Westermayr
Incorporating Long-Range Interactions via the Multipole Expansion into Ground and Excited-State Molecular Simulations.
arXiv: 2502.21045, 2025. DOI - S. Wesely, E. Hofer, R. Curth, S. Paryani, N. Mills, O. Ueberschär, J. Westermayr
Artificial intelligence for objective assessment of acrobatic movements: How to apply machine learning for identifying tumbling elements in cheer sports.
arXiv: 2503.04764, 2025. DOI - M. X. Tiefenbacher, B. Bachmair, C. Giuseppe Chen, J. Westermayr, P. Marquetand, J. C. B. Dietschreit, L. González
Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.
arXiv: 2501.16974, 2025. DOI - R. Barrett, C. Ortner, J. Westermayr
Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSets.
arXiv: 2502.12870, 2025. DOI - R. Barrett, J. Westermayr
Actor-Critic Reinforcement Learning for the Search of Critical Points on Excited-State Potentials: Towards Rapid Identification of Conical Intersections.
submitted, 2024. - T. S. Gutleb, R. Barrett, J. Westermayr, C. Ortner
Parameterizing Intersecting Surfaces via Invariants.
arXiv: 1703.03864, 2024. DOI
Publikationen nach Jahr
- R. Jacobs, D. Morgan, S. Attarian, J. Meng, C. Shen, Z. Wu, C. Yijia Xie, J. H. Yang, N. Artrith, B. Blaiszik, G. Ceder, K. Choudhary, G. Csanyi., E. Dogus Cubuk, B. Deng, R. Drautz, X. Fu, J. Godwin, V. Honavar, O. Isayev, A. Johansson, B. Kozinsky, S. Martiniani, S. Ping Ong, I. Poltavsky, K. J. Schmidt, S. Takamoto, A. P. Thompson, J. Westermayr, B. M. Wood
A practical guide to machine learning interatomic potentials – Status and future.
Curr. Opin. Solid State Mater. Sci. 2025, 35, 101214. DOI
- S. Mausenberger, C. Müller, A. Tkatchenko, P. Marquetand, L. González, J. Westermayr
SpaiNN: equivariant message passing for excited-state nonadiabatic molecular dynamics.
Chem. Sci. 2024, 15, 15880. DOI - R. Barrett, J. Westermayr
Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of Molecules.
J. Phys. Chem. Lett. 2024, 15, 349. DOI - T. Oestereich, R. Tonner-Zech, J. Westermayr
Decoding energy decomposition analysis: Machine-learned Insights on the impact of the density functional on the bonding analysis.
J. Comput. Chem. 2024, 45, 368. DOI
- W. G. Stark, J. Westermayr, O. A. Douglas-Gallardo, J. Gardner, S. Habershon, R. J. Maurer
Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities.
J. Chem. Phys. C 2023, 127, 24168. DOI - B. Mondal, J. Westermayr, R. Tonner-Zech
Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors.
J. Chem. Phys. 2023, 159, 104702. DOI - J. Westermayr, J. Gilkes, R. Barrett, R. J. Maurer
High-throughput property-driven generative design of functional organic molecules.
Nat. Comput. Sci. 2023, 3, 139. DOI
- K. Cseh, H. Geisler, K. Stanojkovska, J. Westermayr, P. Brunmayr, D. Wenisch, N. Gajic, M. Hejl, M. Schaier, G. Koellensperger, M. A. Jakupec, P. Marquetand, W. Kandioller
Arene Variation of Highly Cytotoxic Tridentate Naphthoquinone-Based Ruthenium(II) Complexes and In-Depth In Vitro Studies.
Pharmaceutics 2022, 14, 2466. DOI - J. Westermayr, S. Chaudhuri, A. Jeindl, O. T. Hofmann, R. J. Maurer
Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic–inorganic interfaces.
Digit. Discov. 2022, 1, 463. DOI - J. Westermayr, S. Chaudhuri, A. Jeindl, O. T. Hofmann, R. J. Maurer
Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic–inorganic interfaces.
Digit. Discov. 2022, 1, 463. 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. 2022, 14, 914. 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. 2022, 13, 3812. DOI
- J. Westermayr, R. J. Maurer
Physically inspired deep learning of molecular excitations and photoemission spectra.
Chem. Sci. 2021, 12, 10755. DOI - J. Westermayr, M. Gastegger, K. T. Schütt, R. J. Maurer
Perspective on integrating machine learning into computational chemistry and materials science.
J. Chem. Phys. 2021, 154, 230903. DOI - H. Geisler, J. Westermayr, K. Cseh, D. Wenisch, V. Fuchs, S. Harringer, S. Plutzar, N. Gajic, M. Hejl, M. A. Jakupec, P. Marquetand, W. Kandioller
Tridentate 3-Substituted Naphthoquinone Ruthenium Arene Complexes: Synthesis, Characterization, Aqueous Behavior, and Theoretical and Biological Studies.
Inorg. Chem. 2021, 60, 9805. DOI - J. Westermayr, P. Marquetand
Machine learning for electronically excited states of molecules.
Chem. Rev. 2021, 121, 9873. DOI
- J. Westermayr, P. Marquetand
Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space.
J. Chem. Phys. 2020, 153, 154112. DOI - J. Westermayr, P. Marquetand
Machine learning and excited-state molecular dynamics.
Mach. Learn.: Sci. Technol. 2020, 1, 043001. DOI - J. Westermayr, M. Gastegger, P. Marquetand
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics.
J. Phys. Chem. Lett. 2020, 11, 3828. DOI - J. Westermayr, F. A. Faber, A. S. Christensen, O. von Lilienfeld, P. Marquetand
Neural networks and kernel ridge regression for excited states dynamics of CH2NH2+: From single-state to multi-state representations and multi-property machine learning models.
Mach. Learn.: Sci. Technol. 2020, 1, 025009. DOI
- J. Westermayr, M. Gastegger, M. F. S. J. Menger, S. Mai, L. González, P. Marquetand
Machine learning enables long time scale molecular photodynamics simulations.
Chem. Sci. 2019, 10, 8100. DOI
- J. Westermayr, P. O. Dral, P. Marquetand
Learning excited-state properties
In: Quantum Chemistry in the Age of Machine Learning, Elsevier 2023. DOI - J. Westermayr, O. A. Douglas-Gallardo, S. M. Janke, R. J. Maurer
Machine Learning Accelerated Nonadiabatic Dynamics at Metal Surfaces
In: Reference Module in Chemistry, Molecular Sciences and Chemical Engineering, Elsevier 2022. DOI - J. Westermayr, P. Marquetand
Machine Learning for Nonadiabatic Molecular Dynamics
In: Machine Learning in Chemistry: The Impact of Artificial Intelligence, The Royal Society of Chemistry 2020. DOI