Sample selection was conducted in a batch active learning cycle using Bayesian optimization . Active learning is a process by which we iteratively update our surrogate model, the Gaussian process, at each experiment, leveraging the information gained from the latest batch of experiments to guide the selection of new experiments. By doing so, we reduce the surrogate models uncertainty when making new predictions on unseen samples. The selection of new samples is guided by an acquisition function, in our case we chose an expected improvement acquisition function that balances exploring regions of the design space that are unseen, with exploitation, areas that have yielded high performance in previous experiments.