AI-guided approaches in organometallic research: cognition in chemistry

Organometallic chemistry plays a crucial role in catalysis, energy innovation, and medical advancements. However, rational design of metal-ligand systems remains challenging due to complex electronic structures. This review explores how artificial intelligence, alongside density functional theory, is reshaping this field. By training machine learning models on DFT data, researchers have accelerated targeted discovery, and mechanistic analysis with great efficiency. The use of generative algorithms and autonomous lab systems has expanded our ability to navigate chemical spaces rapidly; however, the extent of this progress strongly depends on the availability of high-quality datasets and the incorporation of domain-specific chemical constraints. Through case studies, ranging from green electro catalysts to Schiff base-derived antimicrobial and anticancer agents, this review highlights how AI balances theoretical precision with real-world applicability. Current challenges such as data scarcity and model interpretability are discussed, along with emerging directions aimed at advancing sustainable and intelligent organometallic design.