Tri-modal late fusion of SchNet geometry, ChemBERTa SMILES, and DCN descriptors reaches 0.0207 eV MAE on QM9 U0 atomization energy, a 20.6% gain over a controlled SchNet baseline under 1M parameters.
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Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings
Tri-modal late fusion of SchNet geometry, ChemBERTa SMILES, and DCN descriptors reaches 0.0207 eV MAE on QM9 U0 atomization energy, a 20.6% gain over a controlled SchNet baseline under 1M parameters.