The GNN was trained on our atom database using 10-fold cross-validation. On average, the GNN training process required 186 iterations to converge using a NVIDIA Tesla T4 GPU with an average convergence time of 2.4 hours. For each fold examined, the predicted ATC magnitudes from the GNN achieved excellent agreement with the DFT-calculated test data (Figure ). The fold-averaged R 2 value was 0.797 ± 0.006, and the fold-averaged root-mean-squared-error was 0.0344 ± 0.0004 (reported as fold mean ± standard error). On average, the ATCs required around 0.1 ms to predict using the machine described above, which is considerably faster than the average time required to compute the ATCs via DFT (being an average of 9.5 minutes of computer time per atom in the molecule).