Facundo Costa

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

PhD Supervisor: Albert Bartok-Partay

Contact: Facundo.Costa@warwick.ac.uk

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.

Current Work: Working on a workflow which gives the best initial 'guess' for an arbitrary dislocation quadrupole cell for an arbitrary Bravais lattice, as predicted by linear anisotropy elasticity. The objective in mind being to create a periodic cell which minimises elastic interaction of dislocations, which can be used for ab-initio calculations.