We developed a machine learning (ML) surrogate model of the modified Skarstrom cycle which can predict the process KPIs for any arbitrary combination of DVs and material properties. We use this surrogate model to expedite the calculation of CO2 purity-recovery and energy penalty-productivity Pareto fronts. Similar to, and guided by, our previous efforts in this domain, we use an artificial neural network (ANN) as our choice of ML model. There CRAFTED MOF material properties, and training limits for the 7 DVs are defined as the optimisable ranges given in Table .