We can avoid the shortcomings of classical DOE methods by turning to machine learning methods, which have solved a wide range of challenging modeling and optimization problems in recent decades . Of particular relevance in the DOE context is Bayesian optimization (BO), an algorithm for autonomous complex optimization tasks. In addition to providing efficient optimization of arbitrary functions, BO constructs so-called surrogate models of design targets in the space of the design variables . The surrogate models are constructed sequentially through a data collection policy, known as the acquisition function, which ensures that the new samples are selected to be as informative as possible given the current state of the model. Consequently, BO is more sample efficient compared to traditional DOE methods, and furthermore supports the incorporation of experimental noise, prior knowledge, and parallel experiments through batch acquisition functions . Recently, BO has become a popular tool in computational materials science, where it has been used both as a means of efficient global optimization and to guide materials discovery . BO is also increasingly being used for optimization in DOE applications . Here, we highlight the power of BO's surrogate model aspect, which provides us with insight into lignin chemistry and facilitates easy multi-objective optimization for green chemistry applications.