In our recent Clusterome paper, 123 we presented a large (∼250k), multi-acid-multi-base, atmospherically-relevant, molecular cluster database (available in the ACDB 2.0 repository, see SI). Here, we extract only ∼32k structures of the (H 2 SO 4 =SA) 0-2 (bases) 0-2 clusters (termed Clusteromics I), where bases correspond to ammonia (AM), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA). We trained the KRR potential with the FCHL19 88 molecular representation (via JKML) to examine whether Δ-ML r 2 SCAN-3c∥GFN1-xTB can substitute the single-point r 2 SCAN-3c in the JKCS workflow mentioned in the previous section. Hence, we selected 10 random equilibrium (H 2 SO 4 ) 3 (base) 3 clusters from Xie et al. and predicted their binding electronic energies with the ML model. Figure shows that the learning curve (orange line) rapidly converges to an MAE of 1.11 kcal/mol corresponding to the model trained on the full Clusteromics I (black dotted line). Hence, already 1k structures randomly selected from the Clusteromics I database are enough to train an accurate ML potential and replace the r 2 SCAN-3c step in the above-described ML workflow. The training on 1000 structures with a subsequent test on 10 structures takes ∼5 CPU hours. However, performing r 2 SCAN-3c itself takes only ∼1 CPU hour. Since this QC method is computationally quite fast, using ML does not speed up the process. More useful is, for instance, Δ-ML 𝜔B97X-D∥GFN1-xTB as used in our previous work on the CS of SA-multi-base clusters or even Δ-ML DLPNO∥𝜔B97X-D as suggested in our recent perspective. However, as a proof of concept, we will use r2SCAN-3c in the next section to show the potential ML speed-up by the categorization trick.