A radial basis function (RBF) kernel is selected due to its smooth properties . And finally, we reformulate the surrogate model due to the titration curve's monotonicity. Typically, a GP has its mean function set to zero, as if no better prior is known and the data is pre-processed via standardisation, then this is a good starting point . However, informed by the physics of the system, we wish to model a GP with a linear and monotonically increasing mean function. We achieve this through fitting a linear regressor over the experimental data points and then a GP (with zero mean) is fit to the errors between the linear regressor's predictions and the experimental data points. No pre-processing is required on the data, meaning we can directly train the model on the same scale that the experimentalist is working in. This is equivalent to having a GP with a linear and monotonically increasing mean function and produces the desired model fits, as illustrated in Figure . The scikit-learn implementation of the RBF kernel has a single hyperparameter to be tuned, the horizontal length scale, which controls the periodicity of the fitting function. Finally, to re-emphasise, these ML models are being trained on a feature space, net moles, which is being calculated via a physical model. The results in Figure (and later in Figure too) show that few iterations are required to reach the target pH and highlight the benefit of using such a "physics-informed" approach.