2 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
Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
Rogério Almeida Gouvêa, Pierre-Paul De Breuck, Tatiane Pretto +2
This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent…