| Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet PP De Breuck, G Hautier, GM Rignanese npj computational materials 7 (1), 83, 2021 | 77 | 2021 |
| Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet PP De Breuck, ML Evans, GM Rignanese Journal of Physics: Condensed Matter 33 (40), 404002, 2021 | 44 | 2021 |
| A simple denoising approach to exploit multi-fidelity data for machine learning materials properties X Liu, PP De Breuck, L Wang, GM Rignanese npj Computational Materials 8 (1), 233, 2022 | 10 | 2022 |
| Accurate experimental band gap predictions with multifidelity correction learning PP De Breuck, G Heymans, GM Rignanese Journal of Materials Informatics 2, 10, 2022 | 9 | 2022 |
| Combination of ab initio descriptors and machine learning approach for the prediction of the plasticity mechanisms in β-metastable Ti alloys M Coffigniez, PP De Breuck, L Choisez, M Marteleur, MJ van Setten, ... Materials & Design 239, 112801, 2024 | 6 | 2024 |
| Machine learning materials properties for small datasets PP De Breuck, G Hautier, GM Rignanese APS March Meeting Abstracts 2021, E60. 009, 2021 | 4 | 2021 |
| Influence of Roughness and Coating on the Rebound of Droplets on Fabrics PJ Cruz, PP De Breuck, GM Rignanese, K Glinel, AM Jonas Surfaces and Interfaces 36, 102524, 2023 | 3 | 2023 |
| A generative material transformer using Wyckoff representation PP De Breuck, HA Piracha, MAL Marques arXiv preprint arXiv:2501.16051, 2025 | | 2025 |
| Optical materials discovery and design with federated databases and machine learning V Trinquet, ML Evans, CJ Hargreaves, PP De Breuck, GM Rignanese Faraday Discussions 256, 459-482, 2025 | | 2025 |
| Active Learning: Accelerating Discovery of Optimal Optical Materials through Synergistic Computational Approaches V Trinquet, M Evans, PP De Breuck, GM Rignanese Bulletin of the American Physical Society, 2024 | | 2024 |
| Small datasets, big predictions: learning methods for uncertainty-aware modelling of multi-fidelity material properties PP De Breuck UCL-Université Catholique de Louvain, 2024 | | 2024 |
| Bias-imbalance in data-driven materials science: a case study on MODNet PP De Breuck, M Evans, GM Rignanese APS March Meeting Abstracts 2022, T32. 006, 2022 | | 2022 |
| Vibrational properties of solids: a machine learning approach PP De Breuck, GM Rignanese | | |