We sample the random addition from a uniform distribution between a lower and upper bound (LB/UB), which are hyperparameters of the method. These should be set according to domain expertise. Here, the LB and UB are 0.5 and 1.0%, respectively, where this is a percentage of the total volume of sample to be titrated, i.e., for 10 mL, the random volume will be sampled between 50 𝜇L and 100 𝜇L. After adding acid/base at any step, we ensure to mix the solution appropriately and allow the pH to reach a steady-state value. We have programmed the robot to detect when the pH value reaches a steady state, defined as a deviation of less than 0.02 pH units over a period of at least 15 seconds, and we dynamically control the mixing time accordingly. By this point, we have obtained two initial data points to begin applying a data-driven strategy for pH adjustment. We highlight this as the "physics-informed" ML box in Figure . We present a detailed discussion on the physical chemistry basis and ML below, but for now, we focus on the operations of the pH robot. The robot continues with iterations of suggested titrant volumes from the ML model until we reach the target pH range. Please note that the suggested titrant volumes are checked against pre-defined lower and upper bounds, 𝐿𝐵 !"#$ /𝑈𝐵 !"#$ . These may be hardware-dependent. For example, the upper bound could be the max capacity of the syringe. In our study, the lower bound was set to the minimum volume that the syringe pumps could accurately dispense, 25 𝜇L. (Despite the syringe pumps being able to dose to the 𝜇L, as stated above, this limit is due to the smallest droplet size at the tip of the dosing needles.) At the other limit, 800 𝜇L was used as the upper bound. These are hyperparameters of the method, which should ideally be guided by domain expertise. We assume those adopting our technology concentration of NaOH. Please note, 𝑉 +,"! /𝑉 2+#% refer to the volume of acid/base, respectively.