109 citations · 122 across the 4 of their papers we have counts for
5 papers
Pressure-transferable universal machine-learning potentials: the WBM-MatPES benchmark and Tuga-FF
Pierre-Paul De Breuck, Thalis H. B. da Silva, Antoine Loew +2
Universal machine-learning interatomic potentials hold the promise to replace costly ab initio calculations in materials science workflows. However, the accuracy and transferabilit…
A generative material transformer using Wyckoff representation
Pierre-Paul De Breuck, Hashim A. Piracha, Gian-Marco Rignanese +1
Materials play a critical role in various technological applications. Identifying and enumerating stable compounds, those near the convex hull, is therefore essential. Despite rece…
Optical materials discovery and design with federated databases and machine learning
Victor Trinquet, Matthew L. Evans, Cameron J. Hargreaves +2
Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are inc…
A simple denoising approach to exploit multi-fidelity data for machine learning materials properties
Xiaotong Liu, Pierre-Paul De Breuck, Linghui Wang +1
Machine-learning models have recently encountered enormous success for predicting the properties of materials. These are often trained based on data that present various levels of…
MODNet -- accurate and interpretable property predictions for limited materials datasets by feature selection and joint-learning
Pierre-Paul De Breuck, Geoffroy Hautier, Gian-Marco Rignanese
In order to make accurate predictions of material properties, current machine-learning approaches generally require large amounts of data, which are often not available in practice…