https://www.findaphd.com/search/ProjectDetails.aspx?PJID=80704

University of Bath, UK.

Lead supervisor: Dr Benjamin Morgan, Department of Chemistry 

Second supervisor: Prof Alison Walker, Department of Physics

Lithium-ion batteries are ubiquitous, and will play an increasingly important role in the transition to a low-carbon society. Improvements in technology depend on understanding how the specific chemistries of different materials control their performance in batteries. Using computers to simulate lithium-ion battery materials provides a direct picture of atomic scale physical processes, and this can be used to understand the differences between specific materials.

Much computational research of battery materials has focussed on the dynamics of lithium ions during charging or discharging, and well established methods exist for detailed calculations of simple lithium-diffusion processes.

The kinetics of charging and discharging electrodes, however, depend not only only Li transport, but also on electronic transport. Li-ion electrodes often contain redox active transition-metal centers. Mobile electrons or holes exist as strongly localised polarons, corresponding to conceptual oxidation states, and undergo thermally activated transport.

This project will focus on developing simulation techniques for atomic scale modelling of mixed ionic–electronic transport in Li-ion electrodes, and then applying these to commercially relevant materials. You will learn and use density functional theory, molecular dynamics, and Monte Carlo simulation techniques. The project will use simulation codes developed at the University of Bath, and will also involve the development of new simulation techniques, and writing modelling codes to implement these. The end goal of the project is to simulate charge and discharge processes of lithium-ion electrodes, and to use these to understand the role of material morphology, i.e. surfaces, interfaces, and grain boundaries. The new simulation methods to be developed will have further application in materials where mixed ionic–electronic transport is important, e.g. fuel cell electrodes, perovskite solar cells, and memristive oxides.

Training opportunities:
- Experience developing simulation and analysis modelling codes using compiled and scripting languages (Fortran / Python).
- Experience using a range of modelling techniques applicable: first principles calculations, atomistic molecular dynamics and Monte Carlo.
- Opportunity to develop writing and speaking presentation skills, and to present work at scientific meetings and conferences.
- Opportunity to work on an interdisciplinary project and interact with colleagues in the Department of Chemistry Computational Chemistry section, and in the Department of Physics.
- Opportunity to work with high-performance computing facilities, including national and Bath University facilities.

Anticipated start date: 2 October 2017.