Publications
Showing entries 61 - 80 out of 666
2024
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
Liu CY, Celiberti L, Decker R, Ruotsalainen K, Siewierska K, Kusch M et al. Orbital-overlap-driven hybridization in 3d-transition metal perovskite oxides LaMO3 (M = Ti-Ni) and La2CuO4. Communications Physics. 2024 May 14;7(1):156. doi: 10.1038/s42005-024-01642-5
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
Birschitzky VC, Sokolović I, Prezzi M, Palotás K, Setvín M, Diebold U et al. Machine learning-based prediction of polaron-vacancy patterns on the TiO2(110) surface. npj Computational Materials. 2024 May 6;10(1):89. Epub 2024 Jan 22. doi: 10.1038/s41524-024-01289-4
Wang C, Sombut P, Puntscher L, Jakub Z, Meier M, Pavelec J et al. CO-Induced Dimer Decay Responsible for Gem-Dicarbonyl Formation on a Model Single-Atom Catalyst. Angewandte Chemie - International Edition. 2024 Apr 15;63(16):e202317347. Epub 2024 Jan 31. doi: 10.1002/anie.202317347
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
Domenichini G. Extending the definition of atomic basis sets to atoms with fractional nuclear charge. Journal of Chemical Physics. 2024 Mar 28;160(12):124107. Epub 2024 Mar 25. doi: 10.1063/5.0196383
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
Celiberti L, Fiore Mosca D, Allodi G, Pourovskii LV, Tassetti A, Forino PC et al. Spin-orbital Jahn-Teller bipolarons. Nature Communications. 2024 Mar 18;15:2429. Epub 2023 Jun 27. doi: 10.1038/s41467-024-46621-0
Varrassi L, Liu P, Franchini C. Quasiparticle and excitonic properties of monolayer SrTiO3. Physical Review Materials. 2024 Feb;8(2):024001. doi: 10.1103/PhysRevMaterials.8.024001
Fiore Mosca D, Schnait H, Celiberti L, Aichhorn M, Franchini C. The Mott transition in the 5d1 compound Ba2NaOsO6: a DFT+DMFT study with PAW spinor projectors. Computational Materials Science. 2024 Jan 30;233:112764. Epub 2023 Mar 29. doi: 10.1016/j.commatsci.2023.112764
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(1):45–58. Epub 2023 Nov 14. doi: 10.1038/s42254-023-00655-3
Lemm D, von Rudorff GF, Anatole von Lilienfeld O. Impact of noise on inverse design: the case of NMR spectra matching. Digital Discovery. 2024 Jan;3(1):136-144. 136-144. Epub 2023 Oct 17. doi: 10.48550/arXiv.2307.03969, 10.1039/d3dd00132f
Coretti A, Falkner S, Weinreich J, Dellago C, von Lilienfeld-Toal OA. Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems. KIM Review. 2024;2:3. doi: 10.25950/bfa99422
2023
Kyvala L, Angeletti A, Franchini C, Dellago C. Diffusion and Coalescence of Phosphorene Monovacancies Studied Using High-Dimensional Neural Network Potentials. Journal of Physical Chemistry C. 2023 Dec 14;127(49):23743-23751. doi: 10.1021/acs.jpcc.3c05713
Karandashev K, Weinreich J, Heinen S, Arismendi Arrieta DJ, von Rudorff GF, Hermansson K et al. Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design. Journal of Chemical Theory and Computation. 2023 Dec 12;19(23):8861-8870. doi: 10.48550/arXiv.2307.15563, 10.1021/acs.jctc.3c00822
He S, Huang B, Xiao B, Chang S, Podalko M, Nau WM. Stabilization of Guest Molecules inside Cation-Lidded Cucurbiturils Reveals that Hydration of Receptor Sites Can Impede Binding. Angewandte Chemie - International Edition. 2023 Dec 4;62(49):e202313864. Epub 2023 Oct. doi: 10.1002/anie.202313864
Lemm D, Falk von Rudorff G, Anatole von Lilienfeld O. Improved decision making with similarity based machine learning: applications in chemistry. Machine Learning: Science and Technology. 2023 Dec 1;4(4):045043. doi: 10.1088/2632-2153/ad0fa3, 10.1088/2632-2153/ad0fa3
Vörös D, Angeletti A, Franchini C, Mai S, González L. Adsorption of 4-(N,N-Dimethylamino)-4′-nitrostilbene on an Amorphous Silica Glass Surface. Journal of Physical Chemistry C. 2023 Nov 30;127(47):22964-22974. doi: 10.1021/acs.jpcc.3c05552
Domenichini G, Dellago C. Molecular Hessian matrices from a machine learning random forest regression algorithm. Journal of Chemical Physics. 2023 Nov 21;159(19):194111 . doi: 10.48550/arXiv.2307.16512, 10.1063/5.0169384
Showing entries 61 - 80 out of 666