AI-Driven Expansion and Application of the Alexandria Database
T. Cavignac, J. Schmidt, P.-P. De Breuck, A. Loew, T. F. T. Cerqueira, H.-C. Wang, A. Bochkarev, Y. Lysogorskiy, A. H. Romero, R. Drautz, S. Botti, and M. A. L. Marques.
[Submitted for publication]
Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
R. A. Gouvêa, P.-P. De Breuck, T. Pretto, G.-M. Rignanese, and M. J. L. Santos
npj. Comput. Mater. [under submission]
Generative AI for Crystal Structures: A Review
P.-P. De Breuck, H.-C. Wang, G.-M. Rignanese, S. Botti and M.A.L. Marques
npj. Comput. Mater.
High-Throughput Search for Cubic Chiral Semiconductors: Structural Insights,Database and Stability Trends
P.-P. De Breuck, H.-C Wang, A. Tellez-Mora, M.A.L. Marques and A. H. Romero
[Under preparation]
A generative material transformer using Wyckoff representation
P.-P. De Breuck, H.A. Piracha and M.A.L. Marques
[Submitted for publication]
Optical materials discovery and design via federated databases and machine learning
V. Trinquet, M. Evans, C. Hargreaves, P.-P. De Breuck, and G.-M. Rignanese
Faraday Discuss. (2024)
Combination of ab initio descriptors and machine learning approach for the prediction of the plasticity mechanisms in β-metastable Ti alloys
M. Coffigniez, P.-P. De Breuck, L. Choisez, M. Marteleur, M. J. Van Setten, G. Petretto, G.-M. Rignanese, and P. J. Jacques
Materials & Design 239, 112801 (2024)
Influence of roughness and coating on the rebound of droplets on fabrics
P. J. Cruz, P.-P. De Breuck, G.-M. Rignanese, K. Glinel, A. M. Jonas
Surfaces and Interfaces 36, 102524 (2023)
A simple denoising approach to exploit multi-fidelity data for machine learning materials properties
X. Liu, P.-P. De Breuck, L. Wang, G.-M. Rignanese
npj Comput. Mater. 8, 233 (2022)
Accurate experimental band gap predictions with multifidelity correction learning
P.-P. De Breuck, G. Heymans, G.-M. Rignanese
J Mater. Inf. 2, 10 (2022)
Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet
P.-P. De Breuck, M. L. Evans, G.-M. Rignanese
J. Phys.: Condens. Matter 33, 404002 (2021)
Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet
P.-P. De Breuck, G. Hautier, G.-M. Rignanese
npj Comput. Mater. 7, 83 (2021)
Vibrational properties of solids : a machine learning approach
P.-P. De Breuck, G.-M. Rignanese
Master Thesis (2019)