发表机构
Duke University; Georgia Institute of Technology（杜克大学; 佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究过渡金属催化反应产率预测难题，提出ChemFusion多模态交叉注意力网络，融合电子特征与3D原子坐标，用交叉注意力机制，在交叉偶联库基准测试中性能出色，还能自主学习识别和惩罚空间位阻，提供物理可解释性。
AI 中文摘要
预测过渡金属催化反应的结果因多种物理和化学变量的相互作用而极其复杂。一个长期存在的计算瓶颈是有效地将广泛的电子描述符与反应位点的局部三维几何结构合并。为弥合这种表示差距，我们提出了ChemFusion，一种将传统电子特征与明确的3D原子坐标融合的混合神经网络。该模型使用交叉注意力机制，使全局电子态能够动态关注未池化分子点云内的特定空间约束。在针对各种交叉偶联库进行基准测试时，此方法具有出色的预测性能，远超传统单模态框架。重要的是，提取注意力矩阵表明该架构能自主学习识别和惩罚限制性空间位阻。这提供了基于物理的可解释性，表明有空间意识的网络可以应对标准统计模型通常忽略的复杂反应空间位阻。
英文摘要
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.
Comments10 pages, 4 figures, 2 tables
