5 citations · 5 across the 3 of their papers we have counts for
4 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…
Machine Learning Materials Properties by Encoding Orbital-Projected Density of States
Paulo Pires, Pierre-Paul De Breuck, Mauro Fava +2
Graph neural networks have become the dominant machine-learning architecture for predicting materials properties from crystal structures. Yet the initialization of atomic node feat…
Generative AI for Crystal Structures: A Review
Pierre-Paul De Breuck, Hai-Chen Wang, Gian-Marco Rignanese +2
As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases…
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…