To fully utilise the capabilities of the MAP, the optimiser tenant must be capable of considering measurements of different fidelity and from different data producing methods, including simulations and experiments. In general, data from different sources can have different noise and bias as well as different associated cost of evaluation which need to be considered. Additionally, it can be beneficial to co-optimise multiple objectives at the same time. Therefore, the optimiser tenant should be capable of multi-source multi-objective optimisation to enable a diverse set of optimisation tasks in the MAP. To meet these requirements, the BO procedure implemented in the F2Opt tenant applies a Gaussian process (GP) regression model, which is a smooth and highly flexible model that provides uncertainty estimates and can accommodate small datasets, which is often the starting point in optimisation tasks. These properties make GPs a popular choice of model in BO. To handle data from multiple sources, we consider a multi-task GP and treat each data source as a separate output. This allows for separate predictions for each data source while utilising correlations between data sources to improve the predictions . An acquisition function is then applied to propose new promising electrolyte formulations. In particular the widely used Expected Improvement (EI) function is used. Although not used in this work, multi-objective optimisation can be achieved by transforming multiple objectives into a single objective with a scalarising function , which can then be optimised using standard acquisition functions. Additionally, the optimiser can be configured to initially sample a number of random points before switching to the BO procedure when no initial data are available to fit a model. The F2Opt code was developed in Python using the Pandas , PyTorch and GPyTorch packages and is available online: .