| Recent advances and applications of machine learning in solid-state materials science J Schmidt, MRG Marques, S Botti, MAL Marques npj Computational Materials 5 (1), 1-36, 2019 | 2027 | 2019 |
| Predicting the thermodynamic stability of solids combining density functional theory and machine learning J Schmidt, J Shi, P Borlido, L Chen, S Botti, MAL Marques Chemistry of Materials 29 (12), 5090-5103, 2017 | 350 | 2017 |
| Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning P Borlido, J Schmidt, AW Huran, F Tran, MAL Marques, S Botti npj Computational Materials 6 (1), 1-17, 2020 | 274 | 2020 |
| Roadmap on Machine Learning in Electronic Structure H Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure, 2022 | 152 | 2022 |
| Crystal graph attention networks for the prediction of stable materials J Schmidt, L Pettersson, C Verdozzi, S Botti, MAL Marques Science Advances 7 (49), eabi7948, 2021 | 111 | 2021 |
| Machine Learning the Physical Nonlocal Exchange–Correlation Functional of Density-Functional Theory J Schmidt, CL Benavides-Riveros, MAL Marques Journal of Physical Chemistry Letters, 2019 | 96 | 2019 |
| Machine-learning-assisted determination of the global zero-temperature phase diagram of materials. J Schmidt, N Hoffmann, HC Wang, P Borlido, PJMA Carriço, ... Advanced Materials, e2210788-e2210788, 2023 | 58* | 2023 |
| Predicting the stability of ternary intermetallics with density functional theory and machine learning J Schmidt, L Chen, S Botti, MAL Marques The Journal of chemical physics 148 (24), 2018 | 50 | 2018 |
| Machine learning the derivative discontinuity of density-functional theory J Gedeon, J Schmidt, MJP Hodgson, J Wetherell, CL Benavides-Riveros, ... Machine Learning: Science and Technology 3 (1), 015011, 2021 | 34 | 2021 |
| Reduced density matrix functional theory for superconductors J Schmidt, CL Benavides-Riveros, MAL Marques Physical Review B 99 (22), 224502, 2019 | 32 | 2019 |
| A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals J Schmidt, HC Wang, TFT Cerqueira, MAL Marques Scientific Data 9 (64), 2022 | 28 | 2022 |
| High-throughput study of oxynitride, oxyfluoride and nitrofluoride perovskites H Wang, J Schmidt, S Botti, MAL Marques Journal of Materials Chemistry A, 2021 | 28 | 2021 |
| Superconductivity in antiperovskites N Hoffmann, T Cerqueira, J Schmidt, M Marques npj Computational Materials 8 (1), 2022 | 24 | 2022 |
| Machine learning universal bosonic functionals J Schmidt, M Fadel, CL Benavides-Riveros Physical Review Research 3 (3), L032063, 2021 | 18 | 2021 |
| Improving machine-learning models in materials science through large datasets J Schmidt, TFT Cerqueira, AH Romero, A Loew, F Jäger, HC Wang, ... Materials Today Physics, 101560, 2024 | 17 | 2024 |
| Transfer learning on large datasets for the accurate prediction of material properties N Hoffmann, J Schmidt, S Botti, MAL Marques Digital Discovery, 2023 | 14 | 2023 |
| Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange M Evans, J Bergsma, A Merkys, C Andersen, OB Andersson, D Beltrán, ... Digital Discovery, 2024 | 12 | 2024 |
| Machine-learning correction to density-functional crystal structure optimization R Hussein, J Schmidt, T Barros, MAL Marques, S Botti MRS Bulletin 47 (8), 765-771, 2022 | 12 | 2022 |
| Searching for ductile superconducting Heusler X2YZ compounds N Hoffmann, TFT Cerqueira, P Borlido, A Sanna, J Schmidt, MAL Marques npJ computational materials 9 (138), 2023 | 8 | 2023 |
| Symmetry-based computational search for novel binary and ternary 2D materials HC Wang, J Schmidt, MAL Marques, L Wirtz, AH Romero 2D Materials 10 (3), 035007, 2023 | 7 | 2023 |
Miguel Alexandre Lopes MarquesRC-FEMS, ICAMS, and Faculty of Mechanical Engineering, Ruhr University BochumEmail confirmado em rub.de