|
| | Preview | Author(s) | Title | Type | Issue Date |
| 1 | | Liao, Ke ; Li, Xin-Zheng ; Alavi, Ali ; Grüneis, Andreas | A comparative study using state-of-the-art electronic structure theories on solid hydrogen phases under high pressures | Artikel Article  | 2019 |
| 2 | | Herzog, Basile ; Gallo, Alejandro ; Hummel, Felix ; Badawi, Michael ; Bučko, Tomáš ; Lebègue, Sébastien ; Grüneis, Andreas ; Rocca, Dario | Coupled cluster finite temperature simulations of periodic materials via machine learning | Article Artikel | 4-Apr-2024 |
| 3 | | Arrigoni, Marco ; Madsen, Georg K. H. | Evolutionary computing and machine learning for discovering of low-energy defect configurations | Artikel Article | 2021 |
| 4 | | Borlido, Pedro ; Schmidt, Jonathan ; Huran, Ahmad W. ; Tran, Fabien ; Marques, Miguel A. L. ; Botti, Silvana | Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning | Artikel Article | 2020 |
| 5 | | Oezelt, Harald ; Qu, Luman ; Kovacs, Alexander ; Fischbacher, Johann ; Gusenbauer, Markus ; Beigelbeck, Roman ; Praetorius, Dirk ; Masao, Yano ; Shoji, Tetsuya ; Kato, Akira ; Chantrell, Roy ; Winklhofer, Michael ; Zimanyi, Gergely ; Schrefl, Thomas | Full-spin-wave-scaled stochastic micromagnetism for mesh-independent simulations of ferromagnetic resonance and reversal | Artikel Article | 2022 |
| 6 | | Legenstein, Lukas ; Reicht, Lukas ; Wieser, Sandro ; Simoncelli, Michele ; Zojer, Egbert | Heat transport in crystalline organic semiconductors: coexistence of phonon propagation and tunneling | Article Artikel | 14-Feb-2025 |
| 7 | | Fava, Mauro ; Protik, Nakib Haider ; Li, Chunhua ; Ravichandran, Navaneetha Krishnan ; Carrete, Jesus ; Roekeghem, Ambroise v. ; Madsen, Georg ; Mingo, Natalio ; Broido, David | How dopants limit the ultrahigh thermal conductivity of boron arsenide: a first principles study | Artikel Article | 2021 |
| 8 | | Chen, Siyu ; Wei, Yao ; Monserrat, Bartomeu ; Tomczak, Jan M. ; Poncé, Samuel | Impact of electronic correlations on the superconductivity of high-pressure CeH₉ | Article Artikel | 14-Jan-2026 |
| 9 | | Fried, Henry Phillip ; Barragan-Yani, Daniel ; Libisch, Florian ; Wirtz, Ludger | A machine learning approach to predict tight-binding parameters for point defects via the projected density of states | Article Artikel | 11-Jun-2025 |
| 10 | | Birschitzky, Viktor ; Ellinger, F ; Diebold, Ulrike ; Reticcioli, Michele ; Franchini, Cesare | Machine learning for exploring small polaron configurational space | Article Artikel | 2022 |
| 11 | | Schattauer, Christoph ; Todorović, Milica ; Ghosh, Kunal ; Rinke, Patrick ; Libisch, Florian | Machine learning sparse tight-binding parameters for defects | Article Artikel | 20-May-2022 |
| 12 |  | Birschitzky, Viktor ; Sokolovic, Igor ; Prezzi, Michael ; Palotás, Krisztián ; Setvin, Martin ; Diebold, Ulrike ; Reticcioli, Michele ; Franchini, Cesare | Machine learning-based prediction of polaron-vacancy patterns on the TiO₂(110) surface | Article Artikel   | 2024 |
| 13 | | Lin, Shuyao ; Casillas-Trujillo, Luis ; Tasnádi, Ferenc ; Hultman, Lars ; Mayrhofer, Paul H. ; Sangiovanni, Davide G. ; Koutná, Nikola | Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics | Article Artikel | 2-Apr-2024 |
| 14 | | Parzer, Michael ; Riss, Alexander ; Garmroudi, Fabian ; de Boor, Johannes ; Mori, Takao ; Bauer, Ernst | SeeBand: a highly efficient, interactive tool for analyzing electronic transport data | Article Artikel | 2025 |
