(untitled) (Communications in Computational Chemistry, 2025)
AI-driven reaction modeling in synthetic chemistry faces critical data gaps: the scarcity of mechanistic descriptors and inconsistent experimental protocols, leading models trained on sparse reactant-product pairs to falter in tasks like yield prediction, selectivity control, or condition optimization. Recent advances in mechanism-aware data curation, such as hybrid rule-ML frameworks and computational datasets, demonstrate progress but remain limited to small molecules (≤10 heavy atoms) or gas-phase approximations. Concurrently, robot-based high-throughput experimentation (HTE) platforms standardize protocols for a small number of reaction classes yet lack end-to-end traceability, often omitting workup and followed separation and purification steps. To bridge these gaps, we propose a closed-loop framework integrating computational chemistry, robotic HTE, and multimodal AI to resolve critical reaction modeling tasks. From the perspective of future work, the field necessitates expanded collaboration across the community to tackle complex systems, extend HTE to underrepresented reactions, and align data ontologies. Interdisciplinary collaboration is essential to transition from retrospective pattern recognition to mechanism-driven discovery, anchoring AI in datasets that encode why reactions succeed, not merely what products form.