Regensburg 2022 – wissenschaftliches Programm
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SYNM: From Physics and Big Data to the Design of Novel Materials
SYNM 1: From Physics and Big Data to the Design of Novel Materials
SYNM 1.4: Hauptvortrag
Montag, 5. September 2022, 16:45–17:15, H1
Automated data-driven upscaling of transport properties in materials — •Danny Perez1 and Thomas Swinburne2 — 1Los Alamos National Laboratory, Los Alamos, USA — 2CRNS/CINaM, Marseille, France
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.