This is a front end for a previously reported ML model which predicts the the porosity of a MOF from a metal and a linker SMILES string . These inputs are sequentially passed through three random forests (RFs). Each RF is trained as in binary classification of the porosity of MOFs as above or below an increasing threshold of pore limiting diameter (PLD). The first RF classifies the porosity of a MOF as being porous or non-porous (PLD of greater or less than 2.4 Å). The second classifies porosity as having large or small pores (PLD greater or less than 4.4 Å given porosity > 2.4 Å. The last model classifies porosity pores as being large or very large (PLD greater or less than 5.9 Å given porosity > 4.4 Å). In combination these random forests assign a MOF porosity as one of four categories (porosity < 2.4 Å, porosity < 4.4 Å, porosity < 5.9 Å, and porosity ≥ 5.9 Å).