Showing entries 1 - 20 out of 277


Liu M, Wang J, Hu J, Liu P, Niu H, Yan X et al. Layer-by-layer phase transformation in Ti3O5 revealed by machine-learning molecular dynamics simulations. Nature Communications. 2024 Dec;15(1):3079. doi: 10.1038/s41467-024-47422-1

Bosoni E, Beal L, Bercx M, Blaha P, Blügel S, Bröder J et al. How to verify the precision of density-functional-theory implementations via reproducible and universal workflows. Nature Reviews Physics. 2024 Jan;6:45–58. Epub 2023 Nov 14. doi: 10.1038/s42254-023-00655-3


Riemelmoser S, Verdi C, Kaltak M, Kresse G. Machine Learning Density Functionals from the Random-Phase Approximation. Journal of Chemical Theory and Computation. 2023 Oct 24;19(20):7287-7299. doi: 10.48550/arXiv.2308.00665, 10.1021/acs.jctc.3c00848

Sukurma Z, Schlipf M, Humer M, Taheridehkordi A, Kresse G. Benchmark Phaseless Auxiliary-Field Quantum Monte Carlo Method for Small Molecules: Journal of Chemical Theory and Computation. Journal of Chemical Theory and Computation. 2023 Aug 8;19(15):4921–4934. Epub 2023 Jul. doi:, 10.1021/acs.jctc.3c00322

Taheridehkordi A, Schlipf M, Sukurma Z, Humer M, Grüneis A, Kresse G. Phaseless auxiliary field quantum Monte Carlo with projector-augmented wave method for solids. Journal of Chemical Physics. 2023 Jul 28;159(4):044109. doi: 10.48550/arXiv.2304.14029, 10.1063/5.0156657

Jinnouchi R, Minami S, Karsai F, Verdi C, Kresse G. Proton Transport in Perfluorinated Ionomer Simulated by Machine-Learned Interatomic Potential. Journal of Physical Chemistry Letters. 2023 Apr 13;14(14):3581-3588. doi: 10.1021/acs.jpclett.3c00293

Liu P, Wang J, Avargues N, Verdi C, Singraber A, Karsai F et al. Combining Machine Learning and Many-Body Calculations: Coverage-Dependent Adsorption of CO on Rh(111). Physical Review Letters. 2023 Feb 17;130(7):078001. doi: 10.1103/PhysRevLett.130.078001


Humer M, Harding ME, Schlipf M, Taheridehkordi A, Sukurma Z, Klopper W et al. Approaching the basis-set limit of the dRPA correlation energy with explicitly correlated and projector augmented-wave methods. Journal of Chemical Physics. 2022 Nov 21;157(19):194113. doi: 10.1063/5.0124019

Engel M, Miranda H, Chaput L, Togo A, Verdi C, Marsman M et al. Zero-point renormalization of the band gap of semiconductors and insulators using the projector augmented wave method. Physical Review B. 2022 Sep 1;106(9):094316. doi: 10.1103/PhysRevB.106.094316, 10.48550/arXiv.2205.04265

Liu P, Verdi C, Karsai F, Kresse G. Phase transitions of zirconia: Machine-learned force fields beyond density functional theory. Physical Review B. 2022 Feb 16;105(6):L060102. doi: 10.1103/PhysRevB.105.L060102


de Wijs GA, Kresse G, Havenith RWA, Marsman M. Comparing GIPAW with numerically exact chemical shieldings: The role of two-center contributions to the induced current. Journal of Chemical Physics. 2021 Dec 21;155(23):234101. Epub 2021 Dec 15. doi: 10.1063/5.0069637

Turiansky ME, Alkauskas A, Engel M, Kresse G, Wickramaratne D, Shen J-X et al. Nonrad: Computing nonradiative capture coefficients from first principles. Computer Physics Communications. 2021 Oct;267:108056. doi: 10.1016/j.cpc.2021.108056

Verdi C, Karsai F, Liu P, Jinnouchi R, Kresse G. Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials. npj Computational Materials. 2021 Sep 30;7(1):156. doi: 10.1038/s41524-021-00630-5

Varrassi L, Liu P, Ergönenc Yavas Z, Bokdam M, Kresse G, Franchini C. Optical and excitonic properties of transition metal oxide perovskites by the Bethe-Salpeter equation. Physical Review Materials. 2021 Jul 9;5(7):074601. doi: 10.1103/PhysRevMaterials.5.074601

Liu P, Verdi C, Karsai F, Kresse G. α−β phase transition of zirconium predicted by on-the-fly machine-learned force field. Physical Review Materials. 2021 May 24;5(5):053804. doi: 10.1103/PhysRevMaterials.5.053804

Showing entries 1 - 20 out of 277