We trained three NN models on the full SA-W ωB97X-D/6-31++G(d,p) , SA-W B97-3c , and SA-AM B97-3c databases, while training on both energies and forces. For testing, we used the largest clusters in the database which were excluded from the training, i.e., SA 4 W 5 for the SA-W systems and (SA 8 AM 10 , SA 9 AM 10 , and SA 9 AM 11 ) for the SA-AM system. Figure shows the correlation of the NN-modeled and the QC-calculated energies and forces across all systems. For chemical predictions, these models are quite accurate with RMSD and MAE < 1 kcal/mol, and with a very high correlation with the target method (PCC ≈ 1). The same applies to the force predictions with RMSD and MAE < 1 kcal/mol/ Å. In the case of SA-W, there is almost no apparent difference when modeling the two QC methods (ωB97X-D and B97-3c) due to their high correlation (see SI-3). The SA-AM modeling shows only slightly larger MAE and RMSD than the SA-W modeling, likely for the same reasons as mentioned before, i.e., the SA-AM database complexity appears proportionally compensated by the NN model expansion. Similar to the previous section, note that all MAEs are slightly greater than the validation MAEs (see Figure ) as the testing is performed on the largest clusters while validation is performed on 10% of the data cut from the training database. We have a fast method that quite accurately learns the reference QC method. Figure shows the typical computational times required for optimization and vibrational-frequency calculations for the SA-AM clusters during configurational sampling. Note that the times are multiplied by a factor of 8 as 8 CPUs were used even though the scaling of QC methods with the number of CPUs is not exactly linear. The frequency calculations take up approximately 15-50% of the computational time for the large clusters and up to 80% for the small. The scaling behavior of the B97-3c method is relatively moderate, exhibiting an almost linear trend in contrast to the poor scaling of other QC methods (e.g., ωB97X-D or even the coupled-cluster methods). Regardless, many such calculations must be performed during thorough configurational sampling, which is computationally demanding. Utilizing the above-trained NN model for SA-AM, we also show the times required for geometry optimization with the same optimization criteria as for the QC optimization (see Figure ).