用电子流动匹配预测反应机制，确保质量守恒。
Electron flow matching for generative reaction mechanism prediction obeying conservation laws
- 将反应预测转化为电子重分布问题，用流匹配框架建模。
- 严格遵守质量守恒，避免幻觉错误，提升泛化能力。
- 适合需要可解释性与机制理解的反应预测研究者。
化学反应性的核心在于质量守恒原则，它对保证物理一致性、方程式平衡及指导反应设计至关重要。然而，当前数据驱动的反应产物预测模型通常不遵守这一基本约束。本文将反应预测重新定义为电子重分布问题，采用现代深度生成框架中的流匹配方法，提出FlowER模型。该模型通过强制实现精确的质量守恒，克服了以往方法的局限，解决了幻觉式失败模式，能恢复未见底物骨架的机理反应序列，并在极低数据量下微调即可有效推广至域外反应类别。此外，FlowER还能估计热力学或动力学可行性，在反应预测中展现出一定程度的化学直觉。这一内在可解释的框架，显著推进了数据驱动反应结果预测中预测精度与机理理解之间的鸿沟。
原文摘要 · Abstract (English)
Central to our understanding of chemical reactivity is the principle of mass conservation, which is fundamental for ensuring physical consistency, balancing equations, and guiding reaction design. However, data-driven computational models for tasks such as reaction product prediction rarely abide by this most basic constraint. In this work, we recast the problem of reaction prediction as a problem of electron redistribution using the modern deep generative framework of flow matching. Our model, FlowER, overcomes limitations inherent in previous approaches by enforcing exact mass conservation, thereby resolving hallucinatory failure modes, recovering mechanistic reaction sequences for unseen substrate scaffolds, and generalizing effectively to out-of-domain reaction classes with extremely data-efficient fine-tuning. FlowER additionally enables estimation of thermodynamic or kinetic feasibility and manifests a degree of chemical intuition in reaction prediction tasks. This inherently interpretable framework represents a significant step in bridging the gap between predictive accuracy and mechanistic understanding in data-driven reaction outcome prediction.
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