Showing entries 1 - 20 out of 608


Li Z, Varrassi L, Yang Y, Franchini C, Bellaiche L, He J. Ultrastrong Coupling between Polar Distortion and Optical Properties in Ferroelectric MoBr2O2. Journal of the American Chemical Society. 2024 Jun 5;146(22):15411–15419. Epub 2024 May 23. doi: 10.48550/arXiv.2402.15949, 10.1021/jacs.4c03296

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

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:

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

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;6:45–58. 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


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

Celiberti L, Varrassi L, Franchini C. Pb9Cu(PO4)6O is a charge-transfer semiconductor. Physical Review B. 2023 Nov 15;108(20):L201117. Epub 2023 Aug 22. doi: 10.1103/PhysRevB.108.L201117

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

Showing entries 1 - 20 out of 608