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A dual-modal Vision Cross-Attention architecture is proposed, fusing tabular physical-organic data with 2D molecular topologies to address reaction yield prediction gap and provides a scalable and highly interpretable blueprint for augmenting physical chemistry with deep visual learning.
ChemFusion is presented, a hybrid neural network that fuses conventional electronic features with explicit 3D atomic coordinates and reveals that the architecture autonomously learns to identify and penalize restrictive steric hindrances, demonstrating that spatially aware networks can navigate complex reaction sterics...
This work introduces a parameter-efficient Tri-Branch Modular Fusion Neural Network that synthesizes three orthogonal modalities: 3D spatial geometry, discrete topological grammar, and explicit macroscopic physicochemical descriptors that offers a highly efficient alternative to brute-force parameter scaling.
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