illustrates the collective impact of AL on enhancing GP predictions across all 1800 MOFs, depicting the average R 2 value (red line) at each iteration. In tandem with the improvement in R 2 , we present the corresponding percentage of data savings (blue line), as calculated by Eq. 1. Notably, as AL iterations progress, we observe a consistent rise in the average R 2 values, indicative of the AL criterion's efficacy. Around the 500th AL iteration and beyond, the average R 2 across all GP models reaches 0.99, regardless of the GP mean uncertainty across MOFs. This trend underscores the potency of AL in optimizing dataset efficiency while upholding predictive accuracy, providing valuable insights for refining AL policies and strategies.