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Meeting MS&T22: Materials Science & Technology
Symposium Inference-based Approaches for Material Discovery and Property Optimisation
Presentation Title Comparing High-dose Simulated Irradiation in Tungsten to Experiments
Author(s) Daniel Robert Mason, Max Boleininger, Fredric Granberg, Guanze He, Felix Hofmann, Sergei Dudarev
On-Site Speaker (Planned) Daniel Robert Mason
Abstract Scope Massively overlapping molecular dynamics cascade simulations can be used to efficiently generate microstructures corresponding to high irradiation doses. Recent work has shown that the properties of the microstructures match experimental measurements well, provided we restrict our interest to temperatures below stage III recovery- migration of vacancies. In this talk we will compare properties of nanoscale defects generated by simulated irradiation of tungsten with comparable experimental measurements, to test the quality of the microstructures produced. We show that not only does simulated irradiation reproduce physical mechanisms responsible for the observed saturation effects, we can also predict hydrogen retention capacity, thermal diffusivity changes, and simulate TEM images which are in good quantitative agreement with the respective experiments. These predictions are essentially parameter-free, requiring only an empirical potential and boundary conditions of stress, temperature and dose, and therefore suggest a physically-informed way to infer properties of the defect microstructure from experimental measurements.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A General Solid Solution Strengthening Model in Multicomponent Alloys
Alloy-agnostic Criteria for Solidification Cracking Susceptibility Evaluation
Comparing High-dose Simulated Irradiation in Tungsten to Experiments
Exploring the Evolution of Irradiation-induced Defects Through Their Energetic Signatures
High Throughput CALPHAD-based Thermodynamic and Kinetic Evaluation of Stainless-steel Solidification
Multi-technique Characterisation of Ion-irradiation Effects on High-pressure-Torsion (HPT) Processed EUROFER-97
Probing the Local Charge Density and Phonon Dynamics by Electron Microscopy
Uncover Hidden Materials Properties with the Lens of Machine Learning
Using Local Thermal Transport Property to Characterize Microstructure of Materials from Additive and Advanced Manufacturing Technologies

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