Publications
Showing entries 1 - 20 out of 285
2024
Faller C, Kaltak M, Kresse G. Density-based long-range electrostatic descriptors for machine learning force fields. Journal of Chemical Physics. 2024 Dec 7;161(21):214701. doi: 10.48550/arXiv.2406.17595, 10.1063/5.0245615
Montero de Hijes P, Dellago C, Jinnouchi R, Kresse G. Density isobar of water and melting temperature of ice: Assessing common density functionals. Journal of Chemical Physics. 2024 Oct 7;161(13):131102 . doi: 10.1063/5.0227514
Hütner JI, Conti A, Kugler D, Mittendorfer F, Kresse G, Schmid M et al. Stoichiometric reconstruction of the Al2O3(0001) surface. Science. 2024 Sept 13;385(6714):1241-1244. doi: 10.1126/science.adq4744
Schmiedmayer B, Kresse G. Derivative learning of tensorial quantities—Predicting finite temperature infrared spectra from first principles. Journal of Chemical Physics. 2024 Aug 28;161(8):084703. doi: 10.48550/arXiv.2404.19674, 10.1063/5.0217243
Sukurma Z, Schlipf M, Humer M, Taheridehkordi A, Kresse G. Toward Large-Scale AFQMC Calculations: Large Time Step Auxiliary-Field Quantum Monte Carlo. Journal of Chemical Theory and Computation. 2024 May 28;20(10):4205–4217. Epub 2024 May 15. doi: 10.48550/arXiv.2403.02542, 10.1021/acs.jctc.4c00304
Jinnouchi R, Karsai F, Kresse G. Machine learning-aided first-principles calculations of redox potentials. npj Computational Materials. 2024 May 20;10(1):107. doi: 10.48550/arXiv.2309.13217, 10.1038/s41524-024-01295-6
Tal A, Marsman M, Kresse G, Anders A, Rodriguez S, Kim K et al. Solving Millions of Eigenvectors in Large-Scale Quantum-Many-Body-Theory Computations. In ISC High Performance 2024 Research Paper Proceedings (39th International Conference): Hamburg, Germany, May 12 - 16, 2024. Hamburg: Prometeus GmbH. 2024 doi: 10.23919/ISC.2024.10528945
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 Apr 9;15:3079. doi: 10.1038/s41467-024-47422-1
Montero de Hijes P, Dellago C, Jinnouchi R, Schmiedmayer B, Kresse G. Comparing machine learning potentials for water: Kernel-based regression and Behler-Parrinello neural networks. Journal of Chemical Physics. 2024 Mar 21;160(11):114107. doi: 10.1063/5.0197105
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
2023
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: https://arxiv.org/abs/2303.04256v1, 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
Ranalli L, Verdi C, Monacelli L, Kresse G, Calandra M, Franchini C. Temperature-Dependent Anharmonic Phonons in Quantum Paraelectric KTaO3 by First Principles and Machine-Learned Force Fields. Advanced Quantum Technologies. 2023 Apr;6(4):2200131. Epub 2023 Feb 22. doi: 10.1002/qute.202200131
Verdi C, Ranalli L, Franchini C, Kresse G. Quantum paraelectricity and structural phase transitions in strontium titanate beyond density functional theory. Physical Review Materials. 2023 Mar;7(3):L030801. doi: 10.48550/arXiv.2211.09616, 10.1103/PhysRevMaterials.7.L030801
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
2022
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
Unzog M, Tal A, Kresse G. X-ray absorption using the projector augmented-wave method and the Bethe-Salpeter equation. Physical Review B. 2022 Oct 18;106(15):155133. doi: 10.1103/PhysRevB.106.155133
Tröster A, Verdi C, Dellago C, Rychetsky I, Kresse G, Schranz W. Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields. Physical Review Materials. 2022 Sept 16;6(9):094408. doi: 10.1103/PhysRevMaterials.6.094408