Recently Liang et al. benchmarked Bayesian optimization techniques for several materials science tasks. They raised awareness of the utility of anisotropic kernels over isotropic kernels . They found that certain algorithms may perform well on certain tasks while performing poorly on others, highlighting the need for a careful task-based choice of models. In addition, they mentioned the computational advantages of random forest models relative to Gaussian processes despite being slightly less efficient overall.