3 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2025
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…
cond-mat.mtrl-sci2025
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…