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David Egger

Prof. Dr.

Collaborating PI

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

Link to MCML Researchers in Highly-Ranked Journals

02.01.2026

MCML Researchers in Highly-Ranked Journals

57 Papers in 2026 Highlight Scientific Impact

Publications @MCML

2026


[2] Top Journal
X. ZhuP. RinkeD. A. Egger
Predicting the Thermal Behavior 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

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