AI inverse co-design enables the simultaneous optimization of catalysts and reactors by directly targeting desired performance metrics rather than relying on trial-and-error exploration. At DeepChemIQ, we couple forward machine learning models, trained on physics-informed descriptors and multiscale simulation data, with diffusion-based generative algorithms to efficiently explore the design space. The forward (predictive) models evaluate activity, selectivity, and stability, while the inverse (generative) framework proposes new catalyst compositions, structures, and reactor operating conditions that meet specified performance targets. Through iterative optimization and active learning, the system converges toward optimal catalyst–reactor combinations under realistic constraints. This closed-loop, AI-driven co-design approach significantly accelerates discovery, reduces development cost, and enables the identification of high-performance solutions that would be difficult to obtain using conventional design strategies.