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Pierre-Paul De Breuck

Observed names: De Breuck, Pierre-Paul, P.-P. De Breuck
ORCID: 0000-0002-3173-2058
Observed ORCID: 0000-0002-3173-2058
OpenAlex ID: A5012557344
Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet
2021 · Pierre-Paul De Breuck, Geoffroy Hautier, et al. · npj Computational Materials · 146
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Generative AI for crystal structures: a review
2025 · Pierre-Paul De Breuck, Hai-Chen Wang, et al. · npj Computational Materials · 56
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Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet
2021 · Pierre-Paul De Breuck, Matthew L Evans, et al. · Journal of Physics Condensed Matter · 49
AI-Driven expansion and application of the Alexandria database
2026 · Théo Cavignac, Jonathan Schmidt, et al. · Journal of Physics Materials · 28
A generative material transformer using Wyckoff representation
2026 · Pierre-Paul De Breuck, Hashim A. Piracha, et al. · npj Computational Materials · 26
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Combination of ab initio descriptors and machine learning approach for the prediction of the plasticity mechanisms in β-metastable Ti alloys
2024 · M. Coffigniez, P.-P. De Breuck, et al. · Materials & Design · 26
A simple denoising approach to exploit multi-fidelity data for machine learning materials properties
2022 · Xiaotong Liu, Pierre-Paul De Breuck, et al. · npj Computational Materials · 21
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Machine learning on multiple topological materials datasets
2025 · Yuqing He, Pierre-Paul De Breuck, et al. · npj Computational Materials · 16
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Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
2026 · Rogério Almeida Gouvêa, Pierre-Paul De Breuck, et al. · npj Computational Materials · 14
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Accurate experimental band gap predictions with multifidelity correction learning
2022 · Pierre-Paul De Breuck, Grégoire Heymans, et al. · Journal of Materials Informatics · 14
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Optical materials discovery and design with federated databases and machine learning
2024 · Victor Trinquet, Matthew L. Evans, et al. · Faraday Discussions · 12
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Influence of roughness and coating on the rebound of droplets on fabrics
2022 · Patrick James Cruz, Pierre-Paul De Breuck, et al. · Surfaces and Interfaces · 9
Machine learning driven exploration of hydride superconductors at ambient pressure
2026 · Paulo R. Pires, Thalis H.B. da Silva, et al. · Computational Materials Today · 6
Pressure-transferable universal machine-learning potentials: the WBM-MatPES benchmark and Tuga-FF
2026 · De Breuck, Pierre-Paul, da Silva, Thalis H. B., et al. · arXiv (Cornell University)
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Machine learning accelerated exploration of the global high pressure phase diagram
2026 · Paulo R. Pires, Pierre-Paul De Breuck, et al. · Materials Today
Machine Learning Materials Properties by Encoding Orbital-Projected Density of States
2026 · Pires, Paulo, De Breuck, Pierre-Paul, et al. · arXiv (Cornell University)
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Optical materials discovery and design with federated databases and machine learning
2026 · Trinquet, Victor, Evans, Matthew L., et al. · NCCR MARVEL
MP feature DB 210321
2021 · De Breuck, Pierre-Paul · Figshare
Cross NMI (on MP)
2020 · Pierre-Paul De Breuck · Figshare
Materials Project "MP_2018.6" MODData
2020 · Pierre-Paul De Breuck · Figshare
feature database for MODNet
2020 · Pierre-Paul De Breuck · Figshare
Works count: 21
Citation count: 423
H-index: 11
I10-index: 11
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