Therefore, upon observing this behaviour with early iterations of the algorithm, we modified the method to detect when we are in the overshoot region. This is defined by the addition of acid, followed by base or vice versa, i.e., when a correction is used and therefore going to form a complex buffer. Following this, the equation presented above is used, only looking at the past two data points and not the full history, to adjust the sample. The algorithm works such that an overshoot is corrected regardless of which direction you approach the titration curve from and also in the very worst-case scenario, if the overshoot occurs again, then the sample can still be recovered. All in all, it is important to consider the complication which arises from using a weak-strong acid/base pair for the titration and a combined physics-informed ML method works effectively here, whereas a black-box, purely exploitative Bayesian Optimiser without knowledge of the chemistry fails.