Kresse, G.

Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set

Kresse, G., & Furthmüller, J. (1996) · Physical Review B

Advanced Chemical Physics StudiesPhysics of Superconductivity and Magnetism

Cited by 123,652

From ultrasoft pseudopotentials to the projector augmented-wave method

Kresse, G., & Joubert, D. (1999) · Physical Review B

Advanced Chemical Physics StudiesThermodynamic and Structural Properties of Metals and Alloys

Cited by 85,307

Reproducibility in density functional theory calculations of solids

Lejaeghere, K., Bihlmayer, G., Björkman, T., Blaha, P., Blügel, S., Blum, V., Caliste, D., Castelli, I. E., Clark, S. J., Dal Corso, A., et al. (2016) · Science

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 1,649

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

Jinnouchi, R., Lahnsteiner, J., Karsai, F., Kresse, G., & Bokdam, M. (2019) · Physical Review Letters

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 635

Ab Initio Study of the H2–H2S/MoS2 Gas–Solid Interface: The Nature of the Catalytically Active Sites

Raybaud, P., Hafner, J., Kresse, G., Kasztelan, S., & Toulhoat, H. (2000) · Journal of Catalysis

Catalysis and Hydrodesulfurization StudiesIndustrial Gas Emission Control

Cited by 413

How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

Bosoni, E., Beal, L., Bercx, M., Blaha, P., Blügel, S., Bröder, J., Callsen, M., Cottenier, S., Degomme, A., Dikan, V., et al. (2023) · Nature Reviews Physics

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 89