1. Train the GP: we fit (or update) the GP model using the current dataset. This model yields both predictions 𝑓(𝒙) and uncertainties 𝜎(𝒙) for any candidate 𝒙 2. Compute acquisition scores: for each candidate in 𝑈, we compute an acquisition function 𝛼(𝒙) 3. Select the next material and pressure point: We choose one point 𝒙 * that maximize 𝛼(𝒙) 4. Measure the true response: We obtain the methane uptake 𝑦 * = 𝑓(𝒙 * ) 5. Augment and repeat 4998 times: We add {𝒙 * , 𝑦 * } to the training set and retrain the GP, iterating until a total training data of 5000 points is achieved.