Overview
This project focuses on the discovery of next generation battery materials through experimental design and compositional exploration. Combining solid-state synthesis, advanced structural characterization, and electrochemical optimization the project will explore novel cathode materials and/or solid electrolytes and offers an opportunity to develop expertise in materials chemistry while collaborating with computational scientists, physicists, and engineers to accelerate clean energy innovation.Rechargeable batteries play a critical role in enabling the global transition towards clean and sustainable energy technologies. Discovery of new high-performance cathode materials and solid electrolytes is the core challenge to advance these technologies. This project involves the experimental design and compositional exploration of a new class of inorganic materials, detailed characterisation of the materials and full-cell level optimisation of the electrochemical properties and understanding of relevant new mechanisms and chemistries.
The project will combine synthetic solid-state chemistry, advanced structural analysis and measurement of physical and electrochemical properties of new cathode materials and solid electrolytes, enabling the successful candidate to develop a diverse experimental skillset in materials chemistry and battery chemistry. The focus will be on the discovery of new materials and structures with enhanced performance, accelerated by working with computational design experts. Owing to the multi-faceted nature of this dynamic project, the student will work closely with computer scientists, inorganic (electro)chemists, physicists, engineers, and material scientists to discover new inorganic cathode materials and solid electrolytes for batteries. This provides an opportunity to a participate in AI-driven discovery.
Qualifications: Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Chemistry, Physics, Engineering, or Materials Science.
This position will remain open until a suitable candidate has been found.
About this opportunity
Rechargeable batteries play a critical role in enabling the global transition towards clean and sustainable energy technologies. Discovery of new high-performance cathode materials and solid electrolytes is the core challenge to advance these technologies. This project involves the experimental design and compositional exploration of a new class of inorganic materials, detailed characterisation of the materials and full-cell level optimisation of the electrochemical properties and understanding of relevant new mechanisms and chemistries.
The project will combine synthetic solid-state chemistry, advanced structural analysis and measurement of physical and electrochemical properties of new cathode materials and solid electrolytes, enabling the successful candidate to develop a diverse experimental skillset in materials chemistry and battery chemistry. The focus will be on the discovery of new materials and structures with enhanced performance, accelerated by working with computational design experts. Owing to the multi-faceted nature of this dynamic project, the student will work closely with computer scientists, inorganic (electro)chemists, physicists, engineers, and material scientists to discover new inorganic cathode materials and solid electrolytes for batteries. This provides an opportunity to a participate in AI-driven discovery.
Qualifications: Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Chemistry, Physics, Engineering, or Materials Science.
This position will remain open until a suitable candidate has been found.
Further reading
- Lim, M. Sonni, et al., High rate capability and cycling stability in multi-domain nanocomposite LiNi1– xTi3/4 xO2 positive electrodes, Adv. Mater. 37 (2025) 2417899.
- Han, et al., Superionic lithium transport via multiple coordination environments defined by two anion packing, Science 383 (2024) 739–745.
M.A. Wright, et al., Accessing Mg-ion storage in V2PS10 via combined cationic-anionic redox with selective bond cleavage, Angew. Chem. Int. Ed. 63 (2024) e202400837.
- Vasylenko et. Al., Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry, Nature Communications 12 (2021) 5561.