发表机构
School of Electrical Engineering, Southeast University（东南大学电气工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MARC-TS通过两阶段框架学习连续反应路径，显著降低过渡态预测误差，并利用路径表示提升优化效率，连接预测与量子化学精修。
AI 中文摘要
过渡态由反应路径定义，然而大多数机器学习方法将其预测为孤立的几何结构。我们引入了MARC-TS，一个两阶段框架，学习连续的、以端点为条件的路径，以任意分辨率查询该路径，并利用局部路径上下文来优化过渡态候选。我们构建了T1x-IRC-8K数据集，包含8,209个反应和1,088,725个路径解析几何结构。在留出反应上，路径模型将完整路径误差相对于端点插值降低了48.4%，定位器实现了0.127 Å的平均对齐结构误差。量子化学优化和振动分析从410个预测中产生了405个频率确认的一阶鞍点候选。在100个反应的微动弹性带比较中，学习路径初始化在100个优化步骤后，有66%的反应达到了联合几何和力目标，而几何插值仅为12%。通过将路径视为可复用的表示而非辅助输出，MARC-TS连接了过渡态预测、机理解释和量子化学精修。
英文摘要
Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-state candidate. We construct T1x-IRC-8K, a dataset of 8,209 reactions and 1,088,725 path-resolved geometries. On held-out reactions, the path model reduced complete-path error by 48.4% relative to endpoint interpolation, and the localizer achieved a mean aligned structural error of 0.127 Å. Quantum-chemical optimization and vibrational analysis yielded 405 frequency-confirmed first-order saddle-point candidates from 410 predictions. In a 100-reaction nudged elastic band comparison, learned-path initialization reached a joint geometry-and-force target for 66% of reactions, compared with 12% for geometric interpolation after 100 optimizer steps. By treating the path as a reusable representation rather than an auxiliary output, MARC-TS connects transition-state prediction, mechanistic interpretation and quantum-chemical refinement.
Comments32 pages, 16 figures
