Accelerated Design of New Sustainable Battery Materials with Artificial Intelligence Methods

The provision of sustainable low-carbon energy is among the most urgent challenges of our time, and poses fundamental, exciting scientific questions. Materials performance lies at the heart of the development of green energy technologies, and computational methods now play a vital role in modelling the properties of energy materials.

However, a full understanding of the atomistic processes within materials and across interfaces that control the performance of energy storage devices such as lithium-ion batteries remains incomplete. Emerging artificial intelligence (AI) and machine learning techniques are powerful tools offering innovative capabilities for studying new battery materials on length scales from individual atoms to tens of nanometres, promising quantum-mechanical accuracy and predictive power, whilst being many orders of magnitude faster than conventional methods.

The vision of this project is the innovative use of cutting-edge machine learning simulation techniques to probe the atomic-level operation of battery materials, thereby enabling a previously missing microscopic understanding and an accelerated design of new sustainable materials with enhanced performance. Following the success of the lithium-ion battery in powering the portable electronics revolution, we will address electric vehicle application objectives of increasing the energy density and charge rates of battery electrodes and solid electrolytes, with a particular focus on how their macroscopic properties can be connected to the microscopic structure. The project will involve the creation of accurate fitting databases and machine-learning-based interatomic potentials to model the underlying atomistic behaviour of novel battery electrodes and solid electrolytes.

No equivalent concerted AI-modelling project on battery materials that inter-links such different expertise is being undertaken within any current IBM-Oxford Studentship project.