| 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 | 51 | 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 | 32 | 2021 |
| Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet. npj Computational Materials, 7 (1): 1–8, 2021 PP De Breuck, G Hautier, GM Rignanese Javed, A., Rehman, MA, Hassan, F., Shahzad, W., Ahmed, S., & Singh (2022 …, 2021 | 11 | 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 | 6 | 2022 |
| Accurate experimental band gap predictions with multifidelity correction learning PP De Breuck, G Heymans, GM Rignanese Journal of Materials Informatics 2, 10, 2022 | 4 | 2022 |
| 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 | 2 | 2023 |
| 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 |
| 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, 112801, 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 |
| A simple denoising approach to exploit multi-fidelity data for machine learning materials properties GM Rignanese, X Liu, PP De Breuck, L Wang | | 2022 |
| 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 |
| Accurateexperimentalbandgap predictionswith multi-fidelity correction learning PP De Breuck, G Heymans, GM Rignanese | | 2022 |
| MODNet--accurate and interpretable property predictions for limited materials datasets by feature selection and joint-learning PP De Breuck, G Hautier, GM Rignanese arXiv preprint arXiv:2004.14766, 2020 | | 2020 |
| Surfaces and Interfaces PJ Cruz, PP De Breuck, GM Rignanese, K Glinel, AM Jonas | | |
| Vibrational properties of solids: a machine learning approach PP De Breuck, GM Rignanese | | |
