With only two data points, fitting any function more complex than a linear regressor is not meaningful. We fit this model, as demonstrated in Figure , extrapolated between the y-limits of pH 1 to 14. The target pH is represented by the dashed red line in Figure , and we can read off the net moles of H3O + /OH -required to reach the set point. This extrapolation aids us in calculating the quantity of acid/base to be dispensed in the subsequent step. However, early experiments showed, and as can be reasoned through a simple thought experiment: A titration curve typically has a sigmoidal shape. If the initially measured pH points are at either end of the titration curve, then the linear extrapolation will be a significant overestimate of the amount of acid or base required to reach the target pH, as some section of the titration curve will show a steep drop off. This overestimation could lead to an overshoot, consequently forming a complex buffer system. To counter this, we employ a dampening function, depicted in Figure . The range of the dampening function is [0, 𝜁] where 𝜁, the dampening constant, is strictly ≤ 1. We used a default value of 𝜁 = 0.7 where lower values of 𝜁 will make the pH adjustment more conservative and therefore, less prone to overshooting. However, too low a value and this will also increase the total time/number of iterations, as the algorithm will take a more incremental step. The maximum of the dampening function, 𝜁, is found at the pH of the equivalence point between the titrant pair and the dampening function passes through 0 at pH 0 14. We developed the form of the dampening function through fitting a rational function approximation and details for this and computing the equivalence point of a weak-strong acid/base system, are in the SI. If we have a strong-strong acid/base pair then their equivalence point will be at pH 7 and a simpler polynomial fit can be used for the dampening function, again obeying the same properties as outlined above. We utilise the mid-point pH of the initial two data points to derive the weight, 𝑤, from the dampening function. Then, we multiply the extrapolated volume to be added by 𝑤 to suggest the titrant volume for the second step. We have once again employed our physical understanding of the system to design the titration algorithm.