The PhD work has been carried out at the Department of Materials Science and Engineering, where Professor Yanjun Li has been the candidates’ supervisor and Dr. Yijiang Xu, SINTEF Industry and Professor Bjørn Holmedal, Department of Materials Science and Engineering, NTNU have been the candidates’ co-supervisor. The Assessment committee consisted of Dr. Emmanuel Clouet, Atomic Energy and Alternative Energies Commission, Paris, France, Professor Malik Wagih, ETH Zürich, Switzerland, and Associate Professor Xu Lu, Department of Mechanical and Industrial Engineering, NTNU, with Assoc. Prof. Lu also serving as the administrator of the Assessment Committee. The PhD work has been fully funded by SFI PhysMet as part of Research area 2 (Scale and process bridging methodologies).

The PhD work of Xuezhou Wang presents a multi-scale modelling approach to the investigation of  atom clustering and precipitation kinetics in 6xxx aluminium alloys, with a special attention to the role and influence of vacancies. Al–Mg–Si (6xxx series) alloys are essential in transportation and construction due to their combination of strength, formability, corrosion resistance, and recyclability. Their mechanical properties are largely determined by precipitation hardening during artificial ageing (AA), where nanoscale precipitates develop and hinder dislocation motion. In industrial processing, however, solution‑treated alloys are often stored at room temperature before AA. During this natural ageing (NA) period, excess quenched‑in vacancies drive the clustering of solute atoms, increasing hardness and influencing the alloy’s subsequent AA response.

This research uses a suite of multiscale modelling tools—atomistic Monte Carlo simulations, mesoscale cluster‑dynamics models, and grain‑scale numerical simulations, supported by machine‑learning methods—to clarify how vacancies migrate, annihilate, and become incorporated into solute clusters during NA. The integrated results provide new insight into the mechanisms controlling solute clustering and hardening, offering a predictive framework that can guide optimization of alloy chemistry and industrial processing parameters for commercial 6xxx‑series aluminium alloys.