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Meeting MS&T22: Materials Science & Technology
Symposium Inference-based Approaches for Material Discovery and Property Optimisation
Presentation Title Exploring the Evolution of Irradiation-induced Defects Through Their Energetic Signatures
Author(s) Charles A. Hirst, Fredric Granberg, Boopathy Kombaiah, Penghui Cao, Scott Middlemas, R. Scott Kemp, Ju Li, Kai Nordlund, Michael Short
On-Site Speaker (Planned) Charles A. Hirst
Abstract Scope Accurate prediction of a material’s properties relies on the comprehensive characterization of all defects within. Yet for irradiated metals the smallest and most numerous defects are unable to be imaged in TEM. Instead of spatial characterization, defects can be detected and quantified through their excess energy. Differential scanning calorimetry (DSC) experiments of neutron-irradiated Ti measure defect densities that are 3 times greater than those determined using TEM. Two energetically-distinct annealing processes are also observed where the established recovery model predicts only one. Comparing calorimetry experiments to molecular dynamics (MD) simulations reveals the mechanism of recovery with dislocation loops gliding through the material, annihilating defects in their path. Positron annihilation spectroscopy (PAS) and XRD experiments will be used to validate the proposed mechanism. Through a combination of multiple techniques the evolution of irradiation-defects can be deduced, demonstrating the power of inference-based approaches to probe radiation damage at the atomic scale.

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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