I will review recent progress on the development and application of advanced atomistic algorithms to simulate chemomechanical systems where local chemistry and long-range stress are tightly coupled, e.g. at the tip of a propagating crack or the core of a dislocation. I will discuss two general approaches (i): hybrid quantum/classical approaches where bond-breaking is treated at the DFT level embedded within a large-scale classical atomistic model to capture elastic relaxation, including recent applications relevant to dislocation motion in tungsten [1] and fracture in diamond [2]; (ii) the construction of machine learning surrogate models either for electronic structure models [3] or at the interatomic potential level, including recent work carried out within the NOMAD Centre for Excellence to massively parallelise the Gaussian approximation potential fitting process [4]. If time permits, I will also discuss the importance of robust uncertainty estimates when using surrogate models, and report some recent efforts in this direction [5, 6].
[1] P. Grigorev, A. M. Goryaeva, M.-C. Marinica, J. R. Kermode, and T. D. Swinburne, Acta Mater. 247 118734 (2023)
[2] J. Brixey, T. Cowie, A. Jardine, J. R. Kermode, In Prep (2023)
[3] L. Zhang et al., npj Comput. Mater. 8 158 (2022)
[4] S. Klawohn, J. R. Kermode and A. P. Bartók, Mach. Learn. Sci. Tech. 4, 015020 (2023)
[5] A. P. Bartók and J. R. Kermode, arXiv:2206.08744 (2022)
[6] I. Best, T. J. Sullivan and J. R. Kermode, In Prep (2023)
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