RxnCLF: Contrastive Transformation-Aware Reaction Foundation Model for Improved Reactivity Prediction

arXiv:2608.06259 · cs.LG · Submitted 2026-08-06 · Read on arXiv

Yiting Zheng, Cheng Fang, Anthony Donofrio, Haote Li

cs.LG

Submitted: 2026-08-06

Comments: 8 pages, 6 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Latent Space — 37× in this paper · explained in 7 episodes
• Embeddings — 17× in this paper · explained in 2 episodes
• Foundation Model — 12× in this paper · explained in 3 episodes
• Contrastive Learning — 8× in this paper · explained in 9 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Generalization — 6× in this paper · explained in 14 episodes
• Representation Learning — 6× in this paper · explained in 3 episodes
• Scaffold — 6× in this paper · explained in 2 episodes
• Augmentation — 4× in this paper · explained in 2 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 8 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Mean Reciprocal Rank — 3× in this paper
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 10 episodes
• Distribution Shift — 1× in this paper · explained in 12 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Graph Isomorphism Network — 1× in this paper
• Hypergraph — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• K-Nearest Neighbor — 1× in this paper
• Robustness — 1× in this paper · explained in 6 episodes
• Self-supervised Pretraining — 1× in this paper · explained in 3 episodes
• UMAP Projection — 1× in this paper · explained in 2 episodes

Sources

• Improving out-of-distribution generalization via multi-task self-supervised pretraining

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