用距离几何流匹配预测化学反应过渡态，加速反应路径发现。
Generating transition states of chemical reactions via distance-geometry-based flow matching
- 在分子距离几何空间中建模反应动态，通过TSDVNet学习生成速度场。
- 在Transition1X上比React-OT提升30%结构准确率，更快收敛CI-NEB优化。
- 可发现新反应路径，对未见分子和反应类型具有强泛化能力。
过渡态（TS）对理解反应机理至关重要，但其探索受限于实验与计算方法的复杂性。本文提出TS-DFM，一种基于距离几何的流匹配框架，可从反应物和产物预测过渡态。该方法在分子距离几何空间中显式捕捉原子间距离的动态变化，设计TSDVNet网络学习生成过渡态几何结构的速度场。在基准数据集Transition1X上，TS-DFM相比先前最优方法React-OT在结构准确性上提升30%，所生成的过渡态作为初始结构可显著加速CI-NEB优化收敛。此外，该方法能识别替代反应路径，在实验中还发现了能量势垒更低的更优过渡态。在RGD1数据集上的测试进一步验证了其在未见分子与反应类型上的强泛化能力，展现出推动反应探索的巨大潜力。
原文摘要 · Abstract (English)
Transition states (TSs) are crucial for understanding reaction mechanisms, yet their exploration is limited by the complexity of experimental and computational approaches. Here we propose TS-DFM, a flow matching framework that predicts TSs from reactants and products. By operating in molecular distance geometry space, TS-DFM explicitly captures the dynamic changes of interatomic distances in chemical reactions. A network structure named TSDVNet is designed to learn the velocity field for generating TS geometries accurately. On the benchmark dataset Transition1X, TS-DFM outperforms the previous state-of-the-art method React-OT by 30\% in structural accuracy. These predicted TSs provide high-quality initial structures, accelerating the convergence of CI-NEB optimization. Additionally, TS-DFM can identify alternative reaction paths. In our experiments, even a more favorable TS with lower energy barrier is discovered. Further tests on RGD1 dataset confirm its strong generalization ability on unseen molecules and reaction types, highlighting its potential for facilitating reaction exploration.
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