error distributions plotted against molecular weight highlights the difference in performance (Fig. ). The machine learning (ML) models have a narrow, symmetric error distribution tightly centered around y = 0 kcal/mol and y = 100% for the absolute and relative errors, respectively. Notably, the error distributions from the ML predictions do not get wider to the right of the molecular weight axis. This contrasts starkly with the TIP3P ΔG error distribution which becomes wider to the right; conversely, TIP3P relative errors tend to be largest around a molecular weight of 100 and become narrower to the extremes of the weight range. In contrast to the ML ΔG errors which have a narrow, symmetric, elongated distribution centered on the y = 0 kcal/mol (perfect prediction) axis, TIP3P ΔG errors are downshifted and centered around y = -1 kcal/mol. Similarly, the TIP3P relative error distribution is much wider than its ML counterpart, albeit not as downshifted from the y = 100% (perfect prediction) axis as the ΔG error distribution. Crucially, the ML models mispredicted the signs of the HFEs in around 2% of cases compared to around 11% for the TI-based workflow with TIP3P water, and around 15 and 17% with OPC and OPC3 water, respectively.