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Linus C. Erhard

Observed names: Erhard, Linus, Erhard, Linus C., Erhard, Linus Carl, Linus C Erhard, Linus Erhard
ORCID: 0000-0003-0219-5801
Observed ORCID: 0000-0003-0219-5801
OpenAlex ID: A5045551910
A machine-learned interatomic potential for silica and its relation to empirical models
2022 · Linus C. Erhard, Jochen Rohrer, et al. · npj Computational Materials · 97
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Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning
2024 · Linus C. Erhard, Jochen Rohrer, et al. · Nature Communications · 77
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Machine-learning interatomic potentials from a users perspective: a comparison of accuracy, speed and data efficiency
2025 · Niklas Leimeroth, Linus C Erhard, et al. · Modelling and Simulation in Materials Science and Engineering · 30
Crystal structure identification with 3D convolutional neural networks with application to high-pressure phase transitions in SiO 2
2024 · Linus C Erhard, Daniel Utt, et al. · Modelling and Simulation in Materials Science and Engineering · 12
LiNbO 3 Coatings on NCM622: Structure and Performance Insights
2025 · Johannes Haust, Yiran Guo, et al. · Advanced Materials Interfaces · 5
Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations
2025 · Linus C. Erhard, Christoph Otzen, et al. · npj Computational Materials · 5
Research data for "A machine-learned interatomic potential for silica and its relation to empirical models"
2022 · Erhard, Linus C., Rohrer, Jochen, et al. · Zenodo (CERN European Organization for Nuclear Research) · 1
Thermodynamics and Kinetics of Ga Surface Segregation in Cu–Ga Nanoparticles across Composition and Particle Size
2026 · Christoforos Eseroglou, Linus C. Erhard, et al. · ChemRxiv
Chemo-mechanical coupling stabilizes mixed $\mathrm{Ag}_{x}\mathrm{Cu}_{1-x}\mathrm{GaSe}_{2}$ solar-cell absorbers: Insights from Monte-Carlo simulations assisted by ab initio informed machine-learning potentials
2026 · Karanikolas, Vasilios, Perera, Delwin, et al. · arXiv (Cornell University)
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Dataset and trained potential used for: "Chemo-mechanical coupling stabilizes mixed Ag$_{x}$Cu$_{1-x}$GaSe$_2$ solar-cell absorbers: Insights from Monte-Carlo simulations assisted by ab initio informed machine-learning potentials"
2026 · Karanikolas, Vasilios, Perera, Delwin, et al. · Zenodo (CERN European Organization for Nuclear Research)
Author Correction: Understanding phase transitions of α-quartz under dynamic compression conditions by machine-learning driven atomistic simulations
2025 · Linus C. Erhard, Christoph Otzen, et al. · npj Computational Materials
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LiNbO3 Coatings on NCM622: Structure and Performance Insights
2025 · Haust, Johannes, Guo, Yiran, et al. · open_UMR Marburg DSpace 10.0 (Philipps-Universität Marburg)
Quantifying Amorphous Structures on the nm Scale: LiNbO3 Coatings in Solid State Batteries
2024 · Johannes Haust, Jürgen Belz, et al. · Microscopy and Microanalysis
Structure analysis on the nm scale: The amorphous LiNbO3 coatings for solid state batteries
2024 · Johannes Haust, Jürgen Belz, et al. · BIO Web of Conferences
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Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning
2023 · Erhard, Linus C., Rohrer, Jochen, et al. · arXiv (Cornell University)
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Works count: 25
Citation count: 228
H-index: 5
I10-index: 4
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