The AL algorithm for the above approach is illustrated in Figure . The adsorbate features that the Gaussian Process (GP) model (𝐹) developed for each MOF uses to make adsorption predictions (adsorption loading N) are adsorbate surrogate Lennard-Jones parameters (effective epsilon (𝜀) and effective sigma (𝜎)), bond length (𝑙), and charge (𝑞). The adsorbate (which is defined by the combination of values of the aforementioned parameters) and fugacity (𝑓) combinations to be iteratively added to the training data are automatically selected by the AL. To commence the AL algorithm for each MOF, we curated an initial set of twenty-six training data points encompassing fugacities, diverse alchemical adsorbates, and their respective adsorption values. These data points were chosen to represent a broad range derived from the training dataset. A sample of the initial training data for a single MOF is available in the project's GitHub repository.