After initialization, the active machine learning procedure consists of three phases being the training of the machine learning model, the selection of new experiments, and the execution and annotation of these experiments (Figure ). The active machine learning query (phase 2) is determined by a so-called acquisition function which is a measure of potential informativeness or optimality. The model needs the most informative next data point, and thus the point where the acquisition function is maximal for the selected query. The query is performed and new data is gathered (phase 3) after which the machine learning model is retrained (phase 1) and now can make improved predictions. This loop is sequentially iterated until an optimum (Bayesian optimization) is found or a sufficiently accurate model (active learning) is obtained.