| Materials Cloud, a platform for open computational science L Talirz, S Kumbhar, E Passaro, AV Yakutovich, V Granata, F Gargiulo, ... Scientific data 7 (1), 299, 2020 | 326 | 2020 |
| AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance SP Huber, S Zoupanos, M Uhrin, L Talirz, L Kahle, R Häuselmann, ... Scientific data 7 (1), 300, 2020 | 258 | 2020 |
| Single-layered hittorf’s phosphorus: a wide-bandgap high mobility 2D material G Schusteritsch, M Uhrin, CJ Pickard Nano letters 16 (5), 2975-2980, 2016 | 252 | 2016 |
| Workflows in AiiDA: Engineering a high-throughput, event-based engine for robust and modular computational workflows M Uhrin, SP Huber, J Yu, N Marzari, G Pizzi Computational Materials Science 187, 110086, 2021 | 125 | 2021 |
| OPTIMADE, an API for exchanging materials data CW Andersen, R Armiento, E Blokhin, GJ Conduit, S Dwaraknath, ... Scientific data 8 (1), 217, 2021 | 96 | 2021 |
| Toward a unified description of battery data S Clark, FL Bleken, S Stier, E Flores, CW Andersen, M Marcinek, ... Advanced Energy Materials 12 (17), 2102702, 2022 | 69 | 2022 |
| The MOLDY short-range molecular dynamics package GJ Ackland, K DʼMellow, SL Daraszewicz, DJ Hepburn, M Uhrin, ... Computer Physics Communications 182 (12), 2587-2604, 2011 | 53 | 2011 |
| Data Management Plans: the Importance of Data Management in the BIG‐MAP Project IE Castelli, DJ Arismendi‐Arrieta, A Bhowmik, I Cekic‐Laskovic, S Clark, ... Batteries & Supercaps 4 (12), 1803-1812, 2021 | 34 | 2021 |
| Euclidean neural networks: e3nn M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ... Version 0.5. 0, 2022 | 28 | 2022 |
| Common workflows for computing material properties using different quantum engines SP Huber, E Bosoni, M Bercx, J Bröder, A Degomme, V Dikan, K Eimre, ... npj Computational Materials 7 (1), 136, 2021 | 27 | 2021 |
| Through the eyes of a descriptor: Constructing complete, invertible descriptions of atomic environments M Uhrin Physical Review B 104 (14), 144110, 2021 | 24 | 2021 |
| Euclidean neural networks: e3nn, 2020 M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ... URL https://doi. org/10.5281/zenodo 5292912, 0 | 16 | |
| Euclidean neural networks: e3nn, April 2022 M Geiger, T Smidt, M Alby, BK Miller, W Boomsma, B Dice, K Lapchevskyi, ... URL https://doi. org/10.5281/zenodo 6459381 (4), 0 | 12 | |
| Materials cloud, a platform for open computational science. Sci Data 7: 299 L Talirz, S Kumbhar, E Passaro, AV Yakutovich, V Granata, F Gargiulo, ... | 8* | 2020 |
| The OPTIMADE Specification C Andersen, R Armiento, E Blokhin, G Conduit, S Dwaraknath, M Evans, ... | 4 | 2020 |
| Predicting non-square 2D dice probabilities GAT Pender, M Uhrin European Journal of Physics 35 (4), 045028, 2014 | 2 | 2014 |
| Machine learning Hubbard parameters with equivariant neural networks M Uhrin, A Zadoks, L Binci, N Marzari, I Timrov arXiv preprint arXiv:2406.02457, 2024 | 1 | 2024 |
| kiwiPy: Robust, high-volume, messaging for big-data and computational science workflows M Uhrin, SP Huber arXiv preprint arXiv:2005.07475, 2020 | 1 | 2020 |
| Data Management Plan M Uhrin, S Waychal, G Pizzi, N Marzari Deliverable D3 1, 0 | 1 | |
| A High‐Throughput Computational Study Driven by the AiiDA Materials Informatics Framework and the PAULING FILE as Reference Database M Uhrin, G Pizzi, N Mounet, N Marzari, P Villars Materials Informatics: Methods, Tools and Applications, 149-170, 2019 | | 2019 |
Nicola MarzariÉcole Polytechnique Fédérale de Lausanne (EPFL), and Paul Scherrer Institut (PSI)Email verificata su epfl.ch
