Here, we note that the most accurate of these estimates, i.e., the one associated with lower variance during training by maximum likelihood, coincides with the minimum validation error. Employing the same notation of Ref. , we can label such error when evaluated on the same validation batches as the free energy estimate -f AVMD V crys (i) . We also note that f AVMD V crys (i) increases at the start of training towards a maximum and then decreases gradually with a rate that correlates with overfitting, which our previous work has shown to coincide with increasing variance in the f BAR V crys (i) estimates . As such, inspired by Ref. we adopt the following unsupervised way of averaging multiple f BAR V crys (i) estimates obtained from multiple maps during training: