Crysmmnet: multimodal representation for crystal property prediction
Abstract Machine Learning models have emerged as a powerful tool for fast and accurate
prediction of different crystalline properties. Exiting state-of-the-art models rely on a single …
prediction of different crystalline properties. Exiting state-of-the-art models rely on a single …
Molecular graph transformer: stepping beyond ALIGNN into long-range interactions
M Anselmi, G Slabaugh, R Crespo-Otero… - Digital …, 2024 - pubs.rsc.org
Graph Neural Networks (GNNs) have revolutionized material property prediction by learning
directly from the structural information of molecules and materials. However, conventional …
directly from the structural information of molecules and materials. However, conventional …