The emergence of machine learning methods capable of training deterministic bijective coordinate transformations that directly map between the probability distributions of two arbitrarily different metastable states of interest, in this case, the polymorphic cell of interest and the Einstein crystal reference state offers a promising alternative to the costly direct sampling of intermediate, unphysical states . We have recently shown that such approaches, coupled with BAR and MBAR reweighting techniques for free energy estimation can yield very accurate free energy differences between conformational states of isolated molecules .