面向通用过渡态生成的反应变换感知流匹配

中文摘要 AI 辅助

过渡态（TS）结构决定了基元化学反应的能垒和机理路径，但其识别计算成本高昂，因为传统鞍点搜索需要昂贵的量子力学计算。近期机器学习方法通过从反应端点信息预测结构加速了TS生成，但这些方法主要学习端点与TS之间的几何对应关系，隐式表示了基元反应背后的结构变换。为解决这一局限，我们提出TransTS——一个基于原子映射反应物-产物对的通用TS生成的反应变换感知框架。TransTS显式学习反应端点间的原子级结构变换，并将其与反应物、TS和产物的统一原子对齐几何表示相结合，实现反应感知的等变TS几何生成。TransTS旨在为后续量子化学精修提供可靠的TS初始猜测，生成的结构不仅通过几何相似性评估，还通过其收敛到验证鞍点及恢复预期反应路径的能力评估。在独立同分布（IID）和零样本分布外（OOD）基准测试中，TransTS展现出更优的TS初始化质量，尤其对未见过的反应分布具有强泛化性。在极具挑战性的GDB-10-rxn和GDB-17-rxn OOD基准测试中，在相同训练机制下，TransTS生成的TS候选物在精修后比现有方法更频繁地收敛到验证鞍点并恢复预期基元反应。扩大反应覆盖范围和模型容量可进一步提升几何保真度和精修结果。

英文摘要

Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding because conventional saddle-point searches require expensive quantum-mechanical calculations. Recent machine-learning approaches have accelerated TS generation by predicting structures from reaction endpoint information, but they primarily learn geometric correspondence between endpoints and TSs, leaving the structural transformations underlying elementary reactions implicitly represented. To address this limitation, we introduce TransTS, a reaction-transformation-aware framework for generalizable TS generation from atom-mapped reactant-product pairs. TransTS explicitly learns atom-level structural transformations between reaction endpoints and integrates them with a unified atom-aligned geometric representation of reactants, TSs and products, enabling reaction-aware equivariant generation of TS geometries. TransTS is designed to provide reliable TS initial guesses for subsequent quantum-chemical refinement, where generated structures are evaluated not only by geometric similarity but also by their ability to converge to validated saddle points and recover the intended reaction pathways. Across IID and zero-shot OOD benchmarks, TransTS demonstrates improved TS initialization quality, with particularly strong generalization to unseen reaction distributions. On the challenging GDB-10-rxn and GDB-17-rxn OOD benchmarks, TransTS generates TS candidates that more frequently converge to validated saddle points and recover the intended elementary reactions after refinement than existing approaches under the same training regime. Scaling reaction coverage and model capacity further improves both geometric fidelity and refinement outcomes.

发表机构

• Shanghai Jiao Tong University（上海交通大学）
• Shanghai Innovation Institute（上海创新研究院）
• East China Normal University（华东师范大学）
• University of Science and Technology of China（中国科学技术大学）

机构由 AI 辅助整理，请以论文原文为准。