The goal of the AL4GAP workflow is to accelerate the development of the GAP model that uses two-body squared exponential and many-body smooth overlap of atomic positions (SOAP) kernel chemical descriptors . This workflow builds upon our previous AL approach that consists of an unsupervised clustering algorithm combined with Bayesian optimization for on-the-fly hyperparameter tuning of the GAP model . A standalone step-by-step tutorial of the AL scheme is provided in the GitHub repository under the subheading "Simple AL4GAP Tutorial" ("Notebook/tutorial.ipynb") along with an accompanying YouTube video . The AL4GAP workflow illustrated in Figure , which details the actions in the GitHub wrapper script named 'driver.py' that deploys compute resources and executes the workflow in ensemble mode Composition space: The workflow takes the user-defined composition space as an input for setting up the sampling task. In the present study, all the composition space is defined by the user in a comma separated value (CSV) format. An example is illustrated for the molten CaCl 2 -NdCl 3 melt system (Listing 1) also found as the 'density.csv' file in the GitHub repository, where a particular mixture composition is converted into its elemental composition. This example is provided to the user for a minimal trial run. The first three column headers correspond to abbreviated elemental symbols and the corresponding fractional composition. The second from last column with label 'T' corresponds to the target experimental measurement temperature in Kelvin. If the temperature is not known it can be left as a zero value as the actual sampling is performed at an elevated temperature. The final column with label 'rho' corresponds to the density in the units of kg. m -3 .