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martina

Martina Ruffino

Research interests

  • Non-equilibrium Science

Biography

Profile

I completed my Physics MSci at King’s in 2018, graduating with a research thesis on Axial Nearest Neighbour Ising (ANNI) models for stacking fault energies in magnesium. I then joined the Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES) Centre for Doctoral Training. I graduated from the Non-Equilibrium Systems MSc in September 2019, and went on to start a PhD in the theory and simulation of condensed matter with the group of Prof. Tony Paxton, in the Physics department.

My current research focuses on the investigation of double twinning in magnesium. Twinned crystals (i.e. crystals presenting separate grains related by mirror symmetry about a boundary plane of shared lattice points) have been extensively studied. However, double twinning, also referred to as non-classical twinning, is not yet well understood, despite experimental evidence that it is an important mechanism, mediating e.g. the formation of rare-earth texture in Mg alloys. My goal is to construct a comprehensive model of double twinning in magnesium, and to propose a growth mechanism based on the mediation of admissible topological defects predicted by symmetry arguments. I will then test this model using density functional theory methods.

PhD Project:

P79: Double twinning in magnesium

Publications:

"Ising-like models for stacking faults in a free electron metal", Martina Ruffino, Guy C.G. Skinner, Eleftherios I. Andritsos and Anthony T. Paxton; October 2020

Research

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Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES)

The mission of CANES is to train future research leaders in the understanding, control and design of systems far from equilibrium, based on rigorous training in theoretical modelling, simulation and data-driven analysis, and a breadth of awareness of common themes across disciplines.

Research

NMS hero banner multi
Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES)

The mission of CANES is to train future research leaders in the understanding, control and design of systems far from equilibrium, based on rigorous training in theoretical modelling, simulation and data-driven analysis, and a breadth of awareness of common themes across disciplines.