In order to screen the entire chemical space we generate additional SQM-data for higher degrees of substitution and use it to fit linear regression models that reproduce the storage energies to within 0.017 and 0.038 kJ/g depending on degree of substitution (Figure ). These models are then used to estimate the storage density of all 230 billion molecules and the 421 million molecules with storage densities higher than 0.30 kJ/g are selected for further study (Figure ). Gradient boosted tree method (LightGBM) models for the storage density and back reaction barrier are trained on new SQM data chosen among the these 421 million molecules, with respective MAEs of 0.026 kJ/g and 16.4 kJ/mol. These models are used to predict the storage densities and barrier heights for all 421 molecules, and 34,241 molecules with storage densities larger than 0.45 kJ/g and high barrier heights are chosen for further study (Figures and). The highest storage density found in this set is 0.58 kJ/g so it is already clear at this point that there are no molecules among the 230 billion molecules considered here with a storage density approaching 1 kJ/g. The storage densities of the 34,241 molecules are computed using SQM and the barrier heights for the 22,258 molecules with SQM storage densities above 0.4 kJ/g are estimates using PM3.