AL is an iterative data selection strategy that uses an acquisition function to select data points for improving a model's performance. In AL, a ML model queries data points where it has high uncertainty or where it expects large gains in accuracy, guided by an acquisition function, thereby minimizing the number of required training samples while maintaining robust predictive capabilities. The integration of AL into the study of MOFs has advanced the field by enabling efficient exploration of vast structural spaces with reduced computational costs. AL has been successfully applied to accelerate molecular simulations for adsorption processes, enhancing the accuracy of isotherm predictions with minimal data acquisition. materials. GP STD selects points with the highest predictive uncertainty, ensuring exploration in regions where the model is least certain, thereby improving its robustness. EI prioritizes points with the highest expected improvement over the current best observation (𝑓 𝑏𝑒𝑠𝑡 ), combining the predicted value and uncertainty to refine predictions in high-potential regions. PI, by contrast, focuses on maximizing the probability of surpassing (𝑓 𝑏𝑒𝑠𝑡 ). Together, these acquisition functions ensure that the framework effectively improves the model's predictive capabilities.