Research Group David Egger
David Egger
is Professor of Theory of Functional Energy Materials at TU Munich.
He conducts research in the area of computational modeling for materials using quantum-mechanical methods, molecular dynamics, and machine learning. His main research focuses on accelerated computational predictions and discovery of new materials for energy conversion and storage technologies.
Recent News @MCML
Publications @MCML
2026
[2]
X. Zhu • P. Rinke • D. A. Egger
Predicting temperature-dependent optoelectronic properties of semiconductor defects with equivariant neural networks.
npj Computational Materials 12.176. May. 2026. DOI
Predicting temperature-dependent optoelectronic properties of semiconductor defects with equivariant neural networks.
npj Computational Materials 12.176. May. 2026. DOI
2025
[1]
F. P. Delgado • F. Simões • L. Kronik • W. Kaiser • D. A. Egger
Machine-Learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces.
Preprint (Feb. 2025). arXiv
Machine-Learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces.
Preprint (Feb. 2025). arXiv
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2025-01-24 - Last modified: 2026-01-02