Two questions motivated this study. First, whether representability errors could be minimized using an ML model compared with simpler and more common pair-wise isotropic force decompositions. Second, whether combining the physical priors of a simpler model and the complexity of an ML model via ∆-learning provided any advantage. The benchmarks presented here suggest an affirmative answer to both questions, albeit in ways that were surprising to the authors. In nearly all scenarios, the ∆-learning models improved on or showed no downside compared with the ML-only counterparts. In the most significant cases, this improvement occurred by correcting the fundamentally unphysical dynamics exhibited by the ML-only models. In contrast, none of the complex models performed significantly better than the pair-wise models despite showing marginally reduced force errors. We con-clude that while the complex potentials do effectively remove force representability errors, these errors were not very large to begin with.