Truncated higher-order contributions could be pertinent for accurate absolute energies, as seen in the previous 16mer data. In practice, relative energy accuracy is of primary importance. Yao et al. trained mbML methanol potentials and analyzed their performances on five (MeOH) 20 isomers. They used a Generative Adversarial Network (GAN) trained on RI-MP2/cc-pVTZ energies with the Coulomb matrix descriptor. Training included 80% of their data sets that contained 844 800 monomers, 74 240 dimers, and 36 864 trimers.