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Julia Westermayr

Showing results (1-10 of 26) with videos related to

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RSC Advances|May 20, 2026
Machine learning for smell: ordinal odor strength prediction of molecular perfumery componentsPeter Fichtelmann, Julia Westermayr
Chemical Reviews|November 19, 2020
Machine Learning for Electronically Excited States of MoleculesJulia Westermayr, Philipp Marquetand
Chemical Science|November 10, 2025
Machine learning spectroscopy to advance computation and analysisJulia Westermayr, P Marquetand
The Journal of Physical Chemistry Letters|January 3, 2024
Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of MoleculesRhyan Barrett, Julia Westermayr
Chemical Science|August 27, 2021
Physically inspired deep learning of molecular excitations and photoemission spectraJulia Westermayr, Reinhard J Maurer
The Journal of Physical Chemistry Letters|April 21, 2020
Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State DynamicsJulia Westermayr, Michael Gastegger, Philipp Marquetand
The Journal of Chemical Physics|September 8, 2023
Machine learning for accelerated bandgap prediction in strain-engineered quaternary III-V semiconductorsBadal Mondal, Julia Westermayr, Ralf Tonner-Zech
Journal of Computational Chemistry|November 1, 2023
Decoding energy decomposition analysis: Machine-learned Insights on the impact of the density functional on the bonding analysisToni Oestereich, Ralf Tonner-Zech, Julia Westermayr
Chemical Science|October 1, 2025
Photochemical deracemization of 2,3-allenoic acids mediated by a sensitizing chiral phosphoric acid catalystMax Stierle, Daniel Bitterlich, Julia Westermayr, et al.
Nature Computational Science|January 4, 2024
High-throughput property-driven generative design of functional organic moleculesJulia Westermayr, Joe Gilkes, Rhyan Barrett, et al.
Pageof 3

Showing results (1-10 of 26) with videos related to

Sort By:
Pageof 3
RSC Advances|May 20, 2026
Machine learning for smell: ordinal odor strength prediction of molecular perfumery componentsPeter Fichtelmann, Julia Westermayr
Chemical Reviews|November 19, 2020
Machine Learning for Electronically Excited States of MoleculesJulia Westermayr, Philipp Marquetand
Chemical Science|November 10, 2025
Machine learning spectroscopy to advance computation and analysisJulia Westermayr, P Marquetand
The Journal of Physical Chemistry Letters|January 3, 2024
Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of MoleculesRhyan Barrett, Julia Westermayr
Chemical Science|August 27, 2021
Physically inspired deep learning of molecular excitations and photoemission spectraJulia Westermayr, Reinhard J Maurer
The Journal of Physical Chemistry Letters|April 21, 2020
Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State DynamicsJulia Westermayr, Michael Gastegger, Philipp Marquetand
The Journal of Chemical Physics|September 8, 2023
Machine learning for accelerated bandgap prediction in strain-engineered quaternary III-V semiconductorsBadal Mondal, Julia Westermayr, Ralf Tonner-Zech
Journal of Computational Chemistry|November 1, 2023
Decoding energy decomposition analysis: Machine-learned Insights on the impact of the density functional on the bonding analysisToni Oestereich, Ralf Tonner-Zech, Julia Westermayr
Chemical Science|October 1, 2025
Photochemical deracemization of 2,3-allenoic acids mediated by a sensitizing chiral phosphoric acid catalystMax Stierle, Daniel Bitterlich, Julia Westermayr, et al.
Nature Computational Science|January 4, 2024
High-throughput property-driven generative design of functional organic moleculesJulia Westermayr, Joe Gilkes, Rhyan Barrett, et al.
Pageof 3