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Preprint Sep 2026

A System-Independent Metadynamics Strategy for Reactive Training Data: Application to Gas-Phase Organic Reactions

General-purpose machine-learning interatomic potentials (MLIPs) for organic reactions need to be accurate on both the minimum energy path (MEP) for static evaluation of basic properties and the broader configurational space for simulating reaction dynamics. Existing general datasets for gas-phase organic reactions rely...

Wan-Run Jiang, Jin-Zhe Zeng, Man-Yi Yang et al. · 0 citations
Preprint Aug 2026

Reaction-Transformation-Aware Flow Matching for Generalizable Transition State Generation

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 accel...

Kai-Peng Zeng, Wen-Xin Zhai, Sheng-Rui Xu et al. · 0 citations

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