Abstract: I will describe recent work to leverage machine learning techniques to construct efficient surrogates for electronic structure models [1] and at the interatomic potential level [2]. The latter benefits from recent work to massively parallelise the Gaussian approximation potential fitting process [3]. I will also discuss the importance of robust uncertainty estimates when using surrogate models and report recent efforts in this direction. The talk will be illustrated with ongoing applications in computational materials science, e.g. dislocation-impurity interactions in tungsten [4] and structural properties of austenitic stainless steels [5] subject to radiation damage. [1] L. Zhang et al., npj Comput. Mater. 8 158 (2022)[2] J. P. Darby, J. R. Kermode, and G. Csányi,  npj Computational Materials 8, 166 (2022).[3] S. Klawohn, J. R. Kermode and A. P. Bartók, Mach. Learn. Sci. Tech. 4, 015020 (2023)[4] P. Grigorev, A. M. Goryaeva, M.-C. Marinica, J. R. Kermode, and T. D. Swinburne, Acta Mater. 247 118734 (2023)[5] L. Shenoy, C. D. Woodgate, J. B. Staunton, A. P. Bartók, C. S. Becquart, C. Domain, and J. R. Kermode arXiv:2309.08689 (2023)