Computational Modelling of Defects in Battery Materials

Solutions need to be found to the issue of ever increasing global energy demand in order preserve carbon based resources and reduce greenhouse emissions. For grid-scale energy supply, promising renewable energy sources, such as solar and wind lack the consistency to directly replace fossil fuels. To address mismatched supply and demand, novel energy storage methods are needed.. One promising technology is storage using rechargeable batteries. Present battery technology is not yet mature enough to provide widespread grid-based storage, but improved battery materials could allow such a technology to be introduced. A second future large-scale use of lithium-ion batteries, to help aid in reduction of global carbon emissions, is electric vehicles. Performance in battery materials needs to be improved in order for electric vehicles to become fully competitive with traditional petrol based vehicles. The issues with present-day battery technology that prevent application in both grid based storage and transport are similar. Of particular importance to vehicle applications are energy density and charging rates. Computational modelling can help provide deeper insight into what makes current high performance battery materials successful; in understanding this behaviour, we may then exploit this knowledge in other materials. These new materials themselves can be optimised. With modern computing power, computational modelling can also be used to screen for new materials that warrant experimental investigation.


In battery materials, the vast majority of potential charge carriers (ions and electrons) are of no interest. Their energy is such that they are not available to participate in the phenomena we are interested in studying, such as intercalation and diffusion through the electrolyte. It is point defect chemistry that gives us a picture of the generalised thermodynamics of the active particles in a battery, leading to insight into cell potential, conductivity and can lead to information about rate performance.

The aims and objectives of this project are to use first principles computational methods to aid in development of materials particularly focussing on defect and non-stoichiometric properties. In addition to performing first-principles calculations to quantify the effect of defects on Li-ion battery materials, this project also involved using this information to develop methods using classical potentials, to describe transport properties of these materials related to their defect and non-stoichiometric chemistry will be explored. In particular, while methods exist to describe charge equilibration in these materials, there is no satisfactory model to describe how variable oxidation states effect charge equilibration, something this project aims to address.

The crossover between this research and the research priorities of EPSRC include interests in computational modelling and theoretical sciences, and its relation to energy and functional materials. Also shared priorities in energy storage, including the complimentary nature of energy generation technologies, particularly wind and solar power, and energy storage technologies such as battery materials are addressed in this project.

It is intended that this project, in developing methods to describe defect and non-stoichiometric chemistries of battery materials will give new insight into potential battery materials and methods of optimisation for existing battery materials.