The 421 million molecules are still far too many to screen using SQM methods, and the results from the linear model of 5-7 molecules indicate some degree of non-linearity (which are the group that the majority of the remaining molecules belong to). Thus, to more accurately predict the storage densities of the remaining molecules, we train a new model using the gradient boosted tree method, LightGBM . The LightGBM model is trained on new SQM data chosen among the remaining 421 million molecules. The new SQM data is chosen, such that the storage densities are evenly distributed between the minimum and maximum storage densities (0.30 kJ/g -0.57 kJ/g) as predicted by the linear regression model (Figure ). The LightGBM model is trained on 39,057 molecules and validated on 6,510 molecules, respectively. Finally, the new model is tested on 6,510 molecules and shown to have an RMSD value of 0.036 kJ/g (Figure ), lower than the 0.050 kJ/g RMSD for the linear regression model for molecules with 5-7 substituents. The LightGBM model is then used to predict storage densities of all 421 million molecules in approximately 10 hours.