Although this removes invariance to permutation, the machine learning model is only trained on a specific landscape with all dimers containing the same molecules, and so maintaining consistent ordering of atoms for every dimer is trivial. The descriptor is also not invariant to permutation of the molecules themselves, however this effect is negligable as only unique dimers are used in the data set. The oxalic acid FIT+DMA+MP2 landscape was selected to test the machine learning approach, as this shows the most dramatic changes relative to FIT+DMA, which should be the most challenging for machine learning methods to capture. We also tested the approach on the FIT+DMA+MP2 landscape of maleic hydrazide for comparison.