Direct air capture (DAC) of CO 2 requires adsorbents that combine high uptake with fast adsorption kinetics, yet whether these properties can be co-optimized remains unresolved. Here we couple automated high-throughput synthesis and characterization of amine-grafted porous polymers with multi-objective Bayesian optimization in a closed loop. Jointly optimizing equilibrium CO 2 uptake and adsorption rate at 400 ppm CO 2 , we discover a kinetics-uptake trade-off described by a linear Pareto curve, uncovering adsorbents with order-of-magnitude higher adsorption kinetics than benchmark adsorbents at half the CO 2 uptake. Three transferable synthesis-performance design rules are derived from the trained Gaussian process (GP) models and grafting with larger polyamines is established as a lever to extend the observed Pareto front. Process modeling shows that these adsorbents achieve twice the productivity of a realistic DAC process compared to the benchmark DAC adsorbent, Lewatit VP OC 1065. Beyond DAC, the framework provides a blueprint for other closed-loop multi-objective materials optimization studies.