We carried out BO using the recently released BOSS code , which provides a Python-based implementation of BO for applications in materials science. BOSS has previously been applied to a range of different problems in materials modeling . The Gaussian processes used by BOSS were assigned uninformative zero priors for the mean functions and radial basis set kernels, which reflect the smoothness of the materials properties, were used for the covariance functions. We initialized the kernel hyperparameters using inverse gamma priors and subsequently updated them during the BO process by maximizing the marginal likelihood. Initial data for the surrogate models was furthermore obtained from a batch of five Sobol points . To account for the measurement uncertainty in the targets and achieve a better statistical fit to the observed data, we incorporated Gaussian noise terms with zero mean in the surrogate models. The standard deviations of the noise terms were chosen to reflect the estimated measurement errors of 5% for the lignin yield and 5-10% for the structural properties.