The practical utility of our approach was demonstrated by training accurate machine-learning force fields for four organic molecules: ethanol, 1,8-naphthyridine, D-alanine, and D-histidine adsorbed on a pristine graphene substrate using the sGDML architecture. We established that the accuracy of atomic force predictions is strongly influenced by the number of orbits in a system. Greater chemical diversity (resulting in more orbits) leads to increased force reconstruction heterogeneity and larger MAE and RMSE values. This trend was further validated across multiple MLFF architectures, including MACE, SO3krates, and sGDML, for the Nacetylphenylalanyl-pentaalanyl-lysine using the results of the TEA Challenge 2023 benchmark.