We employ the following MD modeling protocol to generate the data for the training and testing of the DNN density prediction model at the center of this work. Starting from the simplified molecular-input lineentry system (SMILES) string of a given compound, we employ the OpenBabel code to create a 3-dimensional structure guess, and then pre-optimize it using the MMFF94s force field via steepest descent. We then compute the packing density with the general Amber force field (GAFF) . For this, we obtain the GAFF parameters in automated fashion using the Antechamber toolkit that is part of Amber-Tools , and carry out the MD simulations within the GROMACS package . We employ GROMACS' solvate tool to create a (10 nm) 3 simulation box and fill it with the pre-optimized target molecules. The number of molecules in the simulation box depends on the given molecule size, but a typical system contains around 1,000 molecules (e.g., 972 for benzylcyclopentane). The system is first subjected to a minimization of the internal energy, which is associated with the relaxation of bonds, bond angles, and dihedral bond angles. This is followed by NVT and NPT equilibration steps for 100 and 240 ps, respectively. Both NVT and NPT ensembles use a Nosé-Hoover thermostat at 298.15 K for temperature control. The NPT ensemble uses the Parinello-Rahman barostat for pressure control. We conclude the MD protocol with a final 40 ps NPT production run. We use an MD timestep of 0.2 fs. We obtain the density by averaging the density values of the system at intervals of 0.2 ps during this final run. We note that this protocol is expected to yield kinetically stable amorphous phases rather than thermodynamically stable crystal structures or meta-stable polymorphs. GAFF is known to underestimate the density values compared to those from experiment, especially for high-density compounds . We employ a linear fit between the calculated and experimental values to account for the systematic differences and empirically calibrate the MD results.