| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 103 | 2022 |
| NOMAD: A distributed web-based platform for managing materials science research data M Scheidgen, L Himanen, AN Ladines, D Sikter, M Nakhaee, Á Fekete, ... Journal of Open Source Software 8 (90), 5388, 2023 | 15 | 2023 |
| Density-of-states similarity descriptor for unsupervised learning from materials data M Kuban, S Rigamonti, M Scheidgen, C Draxl Scientific Data 9 (1), 646, 2022 | 13 | 2022 |
| Similarity of materials and data-quality assessment by fingerprinting M Kuban, Š Gabaj, W Aggoune, C Vona, S Rigamonti, C Draxl MRS Bulletin 47 (10), 991-999, 2022 | 4 | 2022 |
| excitingtools: An exciting Workflow Tool A Buccheri, F Peschel, B Maurer, M Voiculescu, DT Speckhard, H Kleine, ... Journal of Open Source Software 8 (85), 5148, 2023 | 2 | 2023 |
| CELL: a Python package for cluster expansion with a focus on complex alloys S Rigamonti, M Troppenz, M Kuban, A Hübner, C Draxl arXiv preprint arXiv:2310.18223, 2023 | 1 | 2023 |
| MADAS--A Python framework for assessing similarity in materials-science data M Kuban, S Rigamonti, C Draxl arXiv preprint arXiv:2403.10470, 2024 | | 2024 |
| Grammar‐based fuzzing of data integration parsers in computational materials science S Müller, JA Sparka, M Kuban, C Draxl, L Grunske Software: Practice and Experience 54 (2), 208-224, 2024 | | 2024 |
