To achieve the above, we sought to incorporate the MOF features as additional input to a GP surrogate model within an AL framework that also selects the most "informative" MOFs to be used in training data generation. However, as the MOF space is inherently high-dimensional-where each MOF can be described as combination of chemical and textural characteristics such as node and linker types, void fraction, surface area, pore sizes, and so forth-it is imperative to reduce the dimensionality of the MOF representation to make AL exploration more efficient.