Commonly, BO will use Gaussian process (GP) to create surrogate models that maps the relationships between the variables and objectives (Figure ). Then the surrogate model is sampled and the output values are passed to an acquisition function that balances the surrogate model predictions and uncertainties to find variable combinations that are likely to lead to optimal solutions (Figure ). The application of GP and BO to optimization of chemical reactions has the advantages of being able to model complex non-linear relationships between multiple variables and to incorporate uncertainty in their predictions, making them suitable to optimize noisy and expensive evaluation functions.