PhD Title: Machine Learning Potentials for Strength Studies in Hexagonal Close Packed Materials

PhD Supervisor: Albert Bartok-Partay

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.