where weights are proportional to w i = exp (f AVMD V crys (i)). This approach enables to obtain running weighted averages f crys that are minimally affected by the large variances seen at the start of training or, in later stages, due to overfitting. Evaluating Eq. 9 during training yields a converging curve of final FE values (see Figures ) that in practice takes into account estimates from all the previous versions of the trained maps, and all of the random validation batches encountered. This approach practically extends the multimap method described in Ref. by introducing a weighting that accounts for model overfitting. In figures 3 and 4, the standard error bars (f BAR V (i) ± SE(i)) were also averaged using Eq. 9.