One of the strategies for carbon capture and storage is to leverage the adsorption properties of nanoporous materials. The carbon emissions of point sources, such as power plants, can be significantly reduced by applying these materials to the post-combustion capture of flue gas. Zeolites , Metal-Organic Frameworks , Zeolitic Imidazolate Frameworks  and Porous Polymer Networks  are examples of promising nanoporous materials which can efficiently trap flue gas molecules in their pores with diameters of a few (tens of) Angstroms. Carbon dioxide, as a small gas molecule with 3.3 Angstroms of kinetic diameter, represents roughly 10% of the composition of flue gas coming out of coal-fired power station exhausts. In order to be a good carbon capture material, the nanoporous structure must not only adsorb flue gas components but preferentially adsorb CO2 as opposed to more abundant flue gas components, such as N2. Therefore, both the absolute adsorbate loading and the relative CO2/N2 selectivity are important performance figures-of-merit to be considered. Millions of possible crystalline nanoporous materials  have been identified for carbon capture, extending far beyond our capability to quantify in silico the adsorption performance of each individual nanoporous structure by brute force calculations. Experimentally fabricating and measuring the adsorption properties of each framework is also unrealistic, due to the time and cost constraints, leading to the requirement for a pre-screening step to improve the resource allocation. In this talk, we present our work on optimising the classification mechanisms for characterizing nanoporous structures, enabling efficient high-throughput screening of materials for carbon capture. Our automated material screening tool leverages cloud resources to spawn multiple computational experiments in parallel to rapidly explore the vast space of relevant nanoporous materials. A full computational experiment comprises, of not only the realisation of Grand Canonical Monte Carlo (GCMC) adsorption simulations, but also a full geometrical and topological characterisation of the material in terms of its crystalline structure as represented by a point cloud of atomic positions. These can be combined with machine learning to accelerate the estimation of adsorption properties based solely on the atomistic structure of materials.