Supervisors: Dr. Albert Bartok-Partay (Phys.), Prof. James Kermode (Eng.), Dr Livia Bartok-Partay (Chem.)

Summary:

In high-performance applications such as aerospace and medical technologies, there is a high demand for specialised materials with high strength-to-weight ratio and superior corrosion resistance. The reliability and improved development of these materials hinges on our atomic-level understanding on how they behave under stress or strain, and how defects in their crystalline structure affect their performance under different temperature-pressure conditions. This PhD project will take advantage of recent developments in machine learning methods, to enable computer modelling of the mechanical behaviour of titanium alloys to produce a machine learning-based interatomic potential and reference database, as well as to assess its performance in strengths applications.

Background:

The rapidly developing research area of machine-learning (ML) interatomic potentials has enabled a step change in our abilities to model materials on the atomistic scale, emerging as a computationally cost-effective and accurate solution to describe materials properties. These enable us to gain accurate insights into materials properties far beyond the length-scales accessible to first-principles density functional theory (DFT), allowing us to simulate extensive defect structures and large-scale shock simulations while retaining high accuracy.

This project aligns with AWE – Nuclear Security Technologies's work on developing precise ML potentials, focusing on modelling strength behaviour in hexagonal close-packed (hcp) phase metals and alloys such as the high-performance titanium-aluminium-vanadium alloy also known as Ti64. The complex low-symmetry crystal structure of these materials leads to a fascinating interplay between twinning and dislocation-mediated deformation, that is not fully understood yet. Atomistic simulations will be crucial for understanding the effects of strain rate, temperature and pressure on their plastic behaviour.