In this equation, 𝑂 𝑠𝑖𝑚 is the simulated value of the observable, and 𝑂 𝑒𝑥𝑝 is the experimental value of that same observable. A negative value of the mean shift indicates that predictions with Sage are improving over Parsley 1.3.0, whereas a positive value indicates that the predictions are regressing. We present this metric along with the kernel density estimate of the distribution of shifts, to visualize changes in improvement over the training sets. Kernel bandwidths are set to the defaults in the scipy package. Figure shows that the mean shifts are negative (indicating improvement) and statistically significant for both aqueous and non-aqueous Δ𝐺 𝑠𝑜𝑙𝑣 , though the effect is most pronounced for aqueous solvation. This indicates that Sage significantly improves predictions of Δ𝐺 𝑠𝑜𝑙𝑣 compared to Parsley 1.3.0. Note that the number of Δ𝐺 𝑠𝑜𝑙𝑣 measurements in the comparison is slightly lower than the total number in the test set; this is due to several Δ𝐺 𝑠𝑜𝑙𝑣 simulations that failed or had errors, and the comparison is only done on the set of free energy calculations which were successful with both force fields.