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RSC Advances
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May 20, 2026
Machine learning for smell: ordinal odor strength prediction of molecular perfumery components
Peter Fichtelmann, Julia Westermayr
Chemical Reviews
|
November 19, 2020
Machine Learning for Electronically Excited States of Molecules
Julia Westermayr, Philipp Marquetand
Chemical Science
|
November 10, 2025
Machine learning spectroscopy to advance computation and analysis
Julia 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 Molecules
Rhyan Barrett, Julia Westermayr
Chemical Science
|
August 27, 2021
Physically inspired deep learning of molecular excitations and photoemission spectra
Julia 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 Dynamics
Julia 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 semiconductors
Badal 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 analysis
Toni 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 catalyst
Max Stierle, Daniel Bitterlich, Julia Westermayr, et al.
Nature Computational Science
|
January 4, 2024
High-throughput property-driven generative design of functional organic molecules
Julia Westermayr, Joe Gilkes, Rhyan Barrett, et al.
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of 3
Search research articles
Search
Showing results (1-10 of 26) with videos related to
Sort By:
Page
of 3
RSC Advances
|
May 20, 2026
Machine learning for smell: ordinal odor strength prediction of molecular perfumery components
Peter Fichtelmann, Julia Westermayr
Chemical Reviews
|
November 19, 2020
Machine Learning for Electronically Excited States of Molecules
Julia Westermayr, Philipp Marquetand
Chemical Science
|
November 10, 2025
Machine learning spectroscopy to advance computation and analysis
Julia 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 Molecules
Rhyan Barrett, Julia Westermayr
Chemical Science
|
August 27, 2021
Physically inspired deep learning of molecular excitations and photoemission spectra
Julia 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 Dynamics
Julia 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 semiconductors
Badal 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 analysis
Toni 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 catalyst
Max Stierle, Daniel Bitterlich, Julia Westermayr, et al.
Nature Computational Science
|
January 4, 2024
High-throughput property-driven generative design of functional organic molecules
Julia Westermayr, Joe Gilkes, Rhyan Barrett, et al.
Page
of 3