Karsten Reuter
Karsten Reuter works on computational materials and chemistry. His joint publications apply machine learning to physical models and material properties. Reuter coauthored Learning Crystallographic Disorder with K. S. Jakob, A. Walsh and J. T. Margraf. The work appeared in Advanced Materials in 2025. Reuter was among five coauthors of Machine Learning the Energetics of Electrified Solid-Liquid Interfaces. The Physical Review Letters paper connects learning methods to the energy of chemical interfaces. With M. Vondrák and J. T. Margraf, Reuter coauthored a study of the limits of charge-equilibration-based machine-learning potentials. The study appeared in npj Computational Materials.
Timeline
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2025
Learning disordered materials
Reuter coauthored Learning Crystallographic Disorder with K. S. Jakob, A. Walsh and J. T. Margraf. The work appeared in Advanced Materials in 2025.
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2025
Models of electrified interfaces
Reuter was among five coauthors of Machine Learning the Energetics of Electrified Solid-Liquid Interfaces. The Physical Review Letters paper connects learning methods to the energy of chemical interfaces.
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2025
Testing machine-learning potentials
With M. Vondrák and J. T. Margraf, Reuter coauthored a study of the limits of charge-equilibration-based machine-learning potentials. The study appeared in npj Computational Materials.
Sources and credits
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