A multi-head cross-attention fusion of CGCNN and SciBERT embeddings improves predicted formation energy, band gap, Fermi energy, and energy above hull compared to the two vanilla models.
Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts
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MatMMFuse: Multi-Modal Fusion model for Material Property Prediction
A multi-head cross-attention fusion of CGCNN and SciBERT embeddings improves predicted formation energy, band gap, Fermi energy, and energy above hull compared to the two vanilla models.