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Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors

B. Mondal, J. Westermayr, and R. Tonner-Zech. The Journal of Chemical Physics, (September 2023)
DOI: 10.1063/5.0159604

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Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductorsB. Mondal, J. Westermayr, and R. Tonner-Zech. The Journal of Chemical Physics, (September 2023)Incorporating Long-Range Interactions via the Multipole Expansion into Ground and Excited-State Molecular SimulationsR. Barrett, J. Dietschreit, and J. Westermayr. (2025)Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction ProbabilitiesW. Stark, J. Westermayr, O. Douglas-Gallardo, J. Gardner, S. Habershon, and R. Maurer. The Journal of Physical Chemistry C, 127 (50): 24168–24182 (December 2023)SpaiNN: equivariant message passing for excited-state nonadiabatic molecular dynamicsS. Mausenberger, C. Müller, A. Tkatchenko, P. Marquetand, L. González, and J. Westermayr. Chem. Sci., 15 (38): 15880-15890 (2024)Parameterizing Intersecting Surfaces via InvariantsT. Gutleb, R. Barrett, J. Westermayr, and C. Ortner. (2024)Decoding energy decomposition analysis: Machine‐learned Insights on the impact of the density functional on the bonding analysisT. Oestereich, R. Tonner‐Zech, and J. Westermayr. Journal of Computational Chemistry, 45 (7): 368–376 (November 2023)Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of MoleculesR. Barrett, and J. Westermayr. The Journal of Physical Chemistry Letters, 15 (1): 349–356 (January 2024)Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentialsM. Tiefenbacher, B. Bachmair, C. Chen, J. Westermayr, P. Marquetand, J. Dietschreit, and L. González. Digital Discovery, 4 (6): 1478-1491 (2025)Transferable Machine Learning Potential X-MACE for Excited States using Integrated DeepSetsR. Barrett, C. Ortner, and J. Westermayr. (2025)

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Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors

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Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors