The trained DNN model mimics -by design -the MD simulations it is derived from. The second column of Tab. I shows the DNN predictions for the MD test data. The benchmark analysis reveals a very good agreement between the MD and DNN results. The correlation coefficient is R 2 = 0.98, both MAPE and RMSPE are around 1%, and MaxAPE is just 5.5%. Most importantly, we find that the DNN prediction errors are significantly smaller than the intrinsic MD errors (by a factor of 4 to 6), which means that the DNN and MD results are statistically indistinguishable. The prediction quality for the MD training data set is essentially identical to the test set shown in Tab. I, indicating that our DNN model is not overfitted and that its predictions are sounds and reliable. Fig. shows the comparison of the MD and DNN results for the 100,000 compounds for which both MD and DNN results were computed with training data shown in blue and test data in red. We note that the inset of Fig. shows clusters of molecules for which the DNN model predicts very similar density values (suggested by the horizontal lines of data points). This indicates a certain incompleteness of the selected feature representation and associated information loss, which suggests that even more accurate models can be achieved using other, more comprehensive descriptor sets (such as 3dimensional molecular descriptors , extended connectivity , hashed topological torsion (HTT) , hashed atom pair (HAP) , or Morgan fingerprints, or a combination thereof). We also test our DNN model on the experimental data set. As the latter exhibits a structural diversity that goes well beyond the relatively narrow scope of the training data used to create the former, it yields unsurprisingly large errors (MAPE = 9.7%, RM-SPE = 13.5%). Nonetheless, the R 2 = 0.89 shows that the DNN model still captures the structure-property relationships to a certain degree, and given appropriate training data, DNN should deliver predictive models for those compound pools as well. With the accuracy of the trained DNN model established, we apply it to the remaining 1.4 million compounds of the screening library introduced in Sec. II B with the expectation of obtaining similar results as MD would yield. The DNN density predictions are summarized in Fig. . The density values of the molecules at hand range from 902 to 1851 kg/m 3 with an average of 1384 kg/m 3 . The results show a t-or Gaussian-like distribution and most of the compounds in the library have density values between 1200 to 1600 kg/m 3 , with only very few examples at the extreme high and low density regime. It is worth noting that these extreme packing density values may be desirable for certain material applications (e.g., light-weight plastics with large strength-todensity ratio or rigid, impact-resistant thermo-plastics). The sparsity of instances for extreme density values emphasizes the valuable role that high-throughput screening studies via physics-based modeling and/or data-derived prediction models can play in the discovery of suitable materials candidates.