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 | 1469 | 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 | 275 | 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), 96, 2020 | 173 | 2020 |

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 | 75 | 2019 |

Roadmap on Machine Learning in Electronic Structure H Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure, 2022 | 68 | 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 | 49 | 2021 |

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 | 38 | 2018 |

Reduced density matrix functional theory for superconductors J Schmidt, CL Benavides-Riveros, MAL Marques Physical Review B 99 (22), 224502, 2019 | 22 | 2019 |

High-throughput study of oxynitride, oxyfluoride and nitrofluoride perovskites H Wang, J Schmidt, S Botti, MAL Marques Journal of Materials Chemistry A, 2021 | 20 | 2021 |

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 | 18 | 2021 |

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 | 13 | 2022 |

Superconductivity in antiperovskites N Hoffmann, T Cerqueira, J Schmidt, M Marques npj Computational Materials 8 (1), 2022 | 11 | 2022 |

Machine learning universal bosonic functionals J Schmidt, M Fadel, CL Benavides-Riveros Physical Review Research 3 (3), L032063, 2021 | 11 | 2021 |

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 | 9* | 2023 |

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 | 7 | 2022 |

Representability problem of density functional theory for superconductors J Schmidt, CL Benavides-Riveros, MAL Marques Physical Review B 99 (2), 024502, 2019 | 5 | 2019 |

Transfer learning on large datasets for the accurate prediction of material properties N Hoffmann, J Schmidt, S Botti, MAL Marques Digital Discovery, 2023 | 2 | 2023 |

Computational screening of materials with extreme gap deformation potentials P Borlido, J Schmidt, HC Wang, S Botti, MAL Marques npj Computational Materials 8 (1), 156, 2022 | 2 | 2022 |

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 | 1 | 2023 |

Machine learning guided high-throughput search of non-oxide garnets J Schmidt, HC Wang, G Schmidt, MAL Marques npj Computational Materials 9 (1), 63, 2023 | 1 | 2023 |