Project summary

Lithium (Li) metal batteries are considered as the next-generation energy-storage systems due to their high theoretical capacity and energy density. However, Li dendrite growth, interfacial instability, and the limited high-voltage stability of liquid electrolytes limit their practical application. Solid-state electrolytes (SEs) offer a promising alternative by improving safety and enabling high-energy-density batteries. Nevertheless, current solid-state Li metal batteries still suffer from limited cycle life and poor rate capability due to low ionic conductivity, excessive electrolyte thickness, high interfacial resistance, and uncontrolled Li dendrite growth, particularly at high current densities. This project aims to accelerate the discovery of new SEs for lithium-ion batteries by integrating large-scale materials databases with first-principles modelling.

Project objectives

The resulting framework will enable the rapid identification and rational design of next-generation SE materials for safer, higher-energy-density lithium-ion batteries.

Project tasks

The project will leverage the existing lithium-ion SEs to analyse trends in ionic conductivity, activation energy, crystal structure, and chemical composition across reported solid electrolytes, while incorporating atomic simulation data to investigate thermodynamic stability, electronic structure, lithium migration pathways, and electrochemical stability. By combining data-driven analysis, computational screening, and machine learning, the research will establish structure–property relationships and identify key descriptors governing fast lithium-ion transport and material stability.

• Conduct data collection from datasets and literature
• Perform molecular simulations and coding
• Develop causal relationships among different parameters to the SE’s performance
• Interpret the results and validate the findings

Expected outcomes

Gaining hands-on experience in coding, data collection and analysis

Proposing solid-state electrolyte candidates with improved performance

References

https://doi.org/10.1039/d3cs00572k

https://doi.org/10.1002/anie.202409327

https://doi.org/10.1002/adma.202510376

Research environment and Supervisory team

This project will be conducted in the PartCat Laboratory at the UNSW School of Chemical Engineering, and will have access to the high-performance computing facilities. The project will be jointly supervised by Prof Rose Amal and Dr Jodie Yuwono. Students will work closely with members of the PartCat research group and collaborators with other universities.