ChemVD: Constructing Open-world Reaction Variable Spaces via Hierarchical Contrastive Alignment

Reaction condition recommendation should construct compact, high-value variable subspaces from expanding candidate sets for more efficient experimental search. However, existing methods trained to recover historical conditions or rank predefined candidates struggle to generalize to unseen reagents under sparse comparative supervision, or to effectively model joint reaction–category–reagent dependencies. To address these limitations, we propose Chemical Variable-space Design (ChemVD), a structure-aware hierarchical cross-modal contrastive learning framework that reframes reaction condition recommendation as open-candidate retrieval and ranking for screening-space construction. ChemVD maps reaction, category, and reagent representations into a shared embedding space through hierarchical contrastive learning that explicitly captures their joint dependencies, while retaining generalization through multi-positive learning and chemically informed constraints. Experiments show that ChemVD generalizes effectively to reagents unseen during training. On HTE-OOD, with a 20% screening budget, ChemVD achieves a mean achievable maximum yield of 68.78% using an 8B-parameter backbone, outperforming a 1.6T-parameter LLM baseline by 20.44 percentage points. By enabling chemically informed, hierarchical ranking over openworld candidates, this work provides a new modeling paradigm for proposing compact variable spaces to support AI-driven scientific discovery.