After we have at least three data points, we are able to fit a Gaussian Process (GP) surrogate model over the titration curve. This is one of the very few models shown to work in a very-low data regime and was the best-performing model in our group's past study . GPs are the workhorse of Bayesian Optimisation (BO), an algorithm used to optimise a black-box function, 𝑓(𝑥), which is expensive to evaluate . Here we could think of minimising 𝑓(𝑛𝑒𝑡 𝑚𝑜𝑙𝑒𝑠) = J𝑡𝑖𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑐𝑢𝑟𝑣𝑒(𝑛𝑒𝑡 𝑚𝑜𝑙𝑒𝑠) -𝑝𝐻 5+67%5 J. The basic recipe for BO is a surrogate model trained over an initial dataset. After this, a cheaper-to-evaluate (than the original 𝑓(𝑥)) acquisition function, 𝛼(𝑥), is evaluated by querying the surrogate model and used to suggest the next sample. The dataset is updated, and the cycle is repeated until a termination condition is reached. In this case, this is done until we are within the target pH range, as shown in Figure . Here, the simplest form of the acquisition form, 𝛼(𝑥), probability of improvement (PI) is selected, as this acquisition function is completely exploitative, and we are trying to reach the pH target in the fewest iterations. Typically, expected improvement (EI), which balances exploration and exploitation is preferred as PI assigns a reward regardless of the magnitude of improvement. This can result in getting stuck in local optima . However, we select PI knowing that there are no local minima to be concerned about due to smooth, monotone nature of a titration curve. This knowledge also helps guide our choice of GP kernel.