To create structure-based feature space (Descriptor FP), we (i) generate FPs from SMILES, and (ii) concatenate bit-strings from step (i) with the net charge of each sample. Before feeding the ML model, it is also critical to double-check for duplicates. In order to determine whether the bit-vector is long enough, we gather only the SMILES of the state QH 2 and then check for duplicates versus bit length. A vector length of 1024 is needed to generate a distinct vector for each sample. Adding the electron charge states (0,±1, ±2) will give us a feature space of 1025 dimensions for each sample.