这篇论文搞了个ChemFusion模型，把电子特征和3D结构结合起来用交叉注意力，预测催化反应产率比老方法准多了。
ChemFusion是一种混合神经网络，融合了传统电子特征与明确的3D原子坐标，通过交叉注意力机制让全局电子状态动态关注未池化分子点云中的空间约束。在多样化交叉偶联反应库上测试，该模型在预测性能上显著超过传统单模态框架。提取注意力矩阵显示，网络自动学习识别并惩罚限制性空间位阻，提供了物理可解释性。
ChemFusion: A Multimodal Cross-Attention Network for Reaction Yield Prediction
Forecasting the outcomes of transition-metal-catalyzed reactions is notoriously complex due to the interplay of diverse physical and chemical variables. A persistent computational bottleneck has been effectively merging broad electronic descriptors with the localized, three-dimensional geometry of the reactive site. To bridge this representation gap, we present ChemFusion, a hybrid neural network that fuses conventional electronic features with explicit 3D atomic coordinates. Using a cross-attention mechanism, the model enables global electronic states to dynamically attend to specific spatial constraints within un-pooled molecular point clouds. When benchmarked against a diverse library of cross-couplings, this approach delivers exceptional predictive performance, decisively surpassing traditional single-modality frameworks. Importantly, extracting the attention matrices reveals that the architecture autonomously learns to identify and penalize restrictive steric hindrances. This provides a physically grounded interpretability, demonstrating that spatially aware networks can navigate complex reaction sterics that standard statistical models typically miss.