发表机构
Korea University; Hankuk University of Foreign Studies（高丽大学; 韩国外国语大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ReCurveflow是一种基于流匹配的框架，以弯曲参考路径为监督并引入离路径校正，在多数数据拆分与评估指标下，较七个基线方法在过渡态几何预测上取得最优结果。
AI 中文摘要
预测化学反应中的过渡态（TS）至关重要，因为它们能为反应机理提供见解。近期关于过渡态预测的研究聚焦于对直线路径进行监督的流匹配，而这类路径与实际反应轨迹并不一致。我们提出一种新颖的基于流匹配的框架ReCurveflow，该框架以从NEB（弹性带方法）衍生的完整分子几何带插值得到的连续弯曲参考路径为监督，学习预测过渡态几何结构。我们还引入了离路径校正，这使ReCurveflow在推理展开过程中遇到离路径几何状态时，能够生成校正速度场，从而更好地抵抗暴露偏差并提升过渡态预测的准确性。在三个数据拆分和六个评估指标下，ReCurveflow在多数拆分-指标组合上相较于七个基线方法取得了最优结果。定性分析进一步表明，ReCurveflow生成的反应轨迹的能量分布与参考NEB路径高度吻合，其提供的初始化能缓解NEB优化瓶颈，且在其学习到的速度场中展现出预期的校正行为。ReCurveflow的代码库已公开于此https URL。
英文摘要
Predicting transition states (TS) in chemical reactions is crucial, as they provide insights into reaction mechanisms. Recent work on TS prediction have focused on flow matching supervised on straight linear paths that do not align with actual reaction trajectories. We propose a novel flow matching-based framework ReCurveflow that learns to predict TS geometries supervised on continuously curved reference paths interpolated from a full NEB-derived band of molecular geometries. We also introduce off-path correction, which grants ReCurveflow with the ability to produce corrective velocity fields when engaged off-path geometry states during inference rollout, leading to better resistance against exposure bias and accuracy in TS prediction. Across three data splits and six evaluation metrics, ReCurveflow achieves the best result on the majority of split-metric combinations against seven baselines. Qualitative analyses further show that ReCurveflow generates reaction trajectories with energy profiles that closely track the reference NEB path, provides initializations that ease the NEB optimization bottleneck, and exhibits the intended corrective behavior in its learned velocity fields. The ReCurveflow codebase is publicly available at https://github.com/dmis-lab/ReCurveflow.
Comments17 pages
