Invited Talk: Danny PerezTitle: Automated data-driven upscaling of transport properties in materials: the results of a multidisciplinary collaboration between physics and mathematics

Transport properties of complex defects are crucial factors that control the performance of many material systems, e.g., the radiation tolerance of materials for nuclear fusion or fission

applications. Characterizing the transport of complex defects is however notoriously tedious and time-consuming, especially as the defects grow, leading to a combinatorial explosion in the number of possible conformations and local transition pathways. I will present a large-scale data-driven approach to automatically obtain reduced-order models of defect evolution, transport coefficients, as well as effective continuum transport equations, from large number of short molecular dynamics (MD) simulations. The optimal MD simulations to carry out are identified on-the-fly using a Bayesian uncertainty quantification framework and automatically executed on a massively-parallel task-execution infrastructure. We show how this microscopic information can be systematically and efficiently upscaled into meso and macro-scale representations that can inform microstructure evolution models. Throughout this talk, I will show how the multidisciplinary environment provided by NSF math institutes such as IPAM and IMSI was instrumental in the development of the solid mathematical foundations that enabled these advances in computational materials physics.