Nat. Comput. Sci. | 面向未见化学反应的鲁棒生成式过渡态模型

TL;DR - A Nature Computational Science study finds generative transition-state models fail sharply on unseen elements and catalysts. Self-supervised pretraining on inexpensive equilibrium conformations improves out-of-distribution accuracy while reducing required reaction data by up to 75%.

• New benchmarks cover periodic-group substitutions and 10 transition metals, exposing severe element-specific generalization failures in React-OT and AEFM.
• Pseudo-reaction pretraining reduced median RMSD from 0.39 Å to 0.19 Å for structurally similar reactions containing unseen transition metals.
• Semi-empirical GFN2-xTB data enabled economical domain adaptation, but generated structures still require DFT optimization to verify first-order saddle points.
• The approach separates learning target-domain chemical geometry from learning reaction mechanisms with scarce transition-state data.