A consortium of UK researchers is building computer models that simulate the atomic-level behaviour of materials inside batteries, solar cells, and fuel cells. The performance of these energy devices is controlled by processes inside bulk materials, across nano-structures, and at interfaces—but these processes remain poorly understood. Without predictive models, developing next-generation energy materials relies on slow, trial-and-error experimentation. This project aims to change that by creating and applying computational techniques that can accurately predict how materials will behave under operating conditions. If successful, the simulations could enable researchers and industry to design materials with a step change in performance, targeting four specific goals: increasing the efficiency and stability of solar cells, boosting the energy density and charge rate of lithium-ion batteries, improving the lifetime of solid oxide fuel cells, and raising the power output of thermoelectric devices. The work is fundamental science—it will not produce a commercial product directly—but it could accelerate the discovery of materials that make clean energy technologies cheaper and more practical, quietly reshaping the energy grids and supply chains that keep society running.
View original technical description
The provision of clean sustainable energy is among the most urgent challenges to society and to the global economy, and poses fundamental, exciting scientific questions. Materials performance lies at the heart of the development and optimisation of green energy technologies, and computational methods now play a vital role in modelling and predicting the structures, properties and reactivity of complex materials. UK science has an enviable position in the international field, and many key techniques and applications were pioneered here. Particular strengths of the UK community have been the ability to harness the full range of techniques from force-field to electronic structure methods, the effective exploitation of high performance computing facilities, the extensive range of applications and the synergistic relationship with experiment. All these aspects will feed into our collaborative project and, indeed, our team has leading programmes involving both technique development and applications, which exploit the latest development in computational hardware and software. The performance of energy storage and conversion devices is controlled by the atomistic and electronic processes within bulk materials, nano-structures, and across interfacial boundaries. These processes remain, however, poorly understood. The vision of this project is therefore to develop and apply predictive techniques for modelling the atomic level operation of energy materials, thereby enabling both academic and industrial communities to develop new materials for the next generations of energy devices with a step change in performance; and thereby addressing specifically the following critical technological objectives, which will push the RCUK energy agenda forward: (i) increasing the efficiency and stability of solar cells; (ii) enhancing the energy density and charge rate of lithium-ion batteries; (iii) improving the performance and lifetime of solid oxide fuel cells, and (iv) increasing the power from thermoelectric devices. To address these ambitious and exciting challenges, we require a concerted and systematic programme combining a range of state-of-the-art simulation methods with new techniques to work on the following major Themes: (a) exploration of materials; (b) nanostructures and interfaces; (c) ionic and electronic transport; and (d) new technique development. Hence, we have brought together a consortium team from the University of Bath, UCL and Daresbury, with wide and complementary experience in the field. There is no equivalent concerted programme inter-linking different expertise being undertaken elsewhere, and hence will be world-leading in this domain. Indeed, the project will ensure that the UK community remains ahead of the international competition in the field.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know