Rapid Automated Optimisation of New Ultrafast Solid Electrolytes

Project ID: 2531ad1508

(You will need this ID for your application)

Research Theme: Energy and Decarbonisation

UCL Lead department: Chemistry

Project Summary:

This project will explore new ultrafast solid electrolytes through advanced automated synthesis and machine learning (ML) to accelerate the implementation, and reduce the environmental impact of, all solid-state batteries.

Although solid-electrolytes (SEs) may replace unsafe flammable liquid electrolytes and increase energy density, current high-performance garnet and argyrodite SEs cannot achieve the necessary ionic conductivity in functional devices due to impeded Li diffusion within and between SE particles. Additionally, sustainability remains a key issue due to (i) the reliance on rare-earth and critical elements and (ii) intensive heat treatment conditions to synthesise the SEs (up to 18 h at 1000 °C).

The DTP research project will combine RAMP’s automated and digital approaches to materials discovery and optimisation. Key areas include: (i) Using LCA to determine system boundaries for sustainable research of new SEs by tailoring the elemental composition and assessing energy budgets. (ii) Creating a systematic database of materials using the robotic RAMP group platform; (iii) Reduce the carbon footprint using patented flash heating approaches to reduce synthesis times to just minutes. (iv) Prepare and test and solid-state batteries; (v) Compare and model structure-composition-property relationships using machine learning to inform the synthesis loop.

The RAMP group is based at the new Marshgate building on UCL East Campus in London’s Olympic Park, with access state-of-the-art facilities, and opportunities to collaborate with scientists supporting real-world applications.

We are looking for a highly motivated and curious researcher with interest in state-of-the-art materials preparation and data processing. Candidates should have (or expect to receive) a 1st or 2:1 degree in materials science/engineering, chemical engineering, chemistry, or related fields. Experience with synthesis and structural characterization (e.g., X-ray diffraction), and/or life-cycle analysis and machine learning. Candidates must demonstrate strong communication skills, independent and team-oriented work capabilities, and a readiness to travel internationally for synchrotron experiments and presentations.