Abstract
Manufacturing kinetically stable products remains a challenge in the agrochemical industry. Current agrochemical formulation design relies on semi-empirical and trial-and-error methods. The inconsistency is caused by the lack of a mechanistic understanding of the formulation, making the design a black-box optimisation problem. In addition, validating the ground truth of the high-dimensional design space is expensive, driving chemists to explore possible solutions using data-driven methods. We proposed a Bayesian optimisation framework employing a Gaussian process as the surrogate model to intelligently guide the screening of the design space. The uniqueness of our framework is the application to the classification task to increase the number of hits of stable formulation recipes. The framework was tested on a provided industry dataset with a focus on emulsifiable concentrates. The performance reached a comparable accuracy with only ~25% of the data being sampled and hit more stable formulations than a Monte Carlo search. Our framework accelerates the discovery of stable recipes and guides formulation screening.