Lansford and coworkers used Chemprop's purely graph-based directed-message passage neural networks (D-MPNNs) to apply a transfer learning approach simultaneously for training a multi-target regression model on a small number of molecules with experimentally measured ℎ .# values and related computed properties. They iteratively constructed an initial dataset of 172 million likely energetic molecules from the PNNL (Pacific Northwest National Laboratory) database employing the ML ANI-1ccx force field (in some instances, DFT) to compute different energetic and geometric properties. Direct models using Chemprop's bond features such as aromaticity, atomic mass and bond type were trained only on impact sensitivities, while other models were co-trained with DFT or other computed properties for small datasets. The co-training technique applied to experimental sensitivities improved the model performance of the physicsbased Mathieu's modeling. Using an ensemble of models and 14 molecules as test set, they predicted the experimental ℎ .# values of PETN and CL-20, and slightly larger ℎ .# values of RDX, HMX, and TNAZ within the modeling error. The ordering of the predicted impact sensitivity follows the measured values for HNS, PYX, TNT, and TATB, while the insensitive nature of DNAN and NQ that have inaccurate experimental ℎ .# values were found.