The direct air capture (DAC) of carbon dioxide from the atmosphere requires sorbents that combine strong adsorption at dilute CO2 conditions with resistance to competitive H2O adsorption and robust structural stability. In this work, we present a stability-aware, multiobjective computational framework that integrates machine learning (ML) and evolutionary algorithm (NSGA-III) optimization to identify optimal candidate metal-organic frameworks (MOFs) for DAC that exhibit the best trade-offs in...