PhD in Machine Learning-Driven Optimization of Ion Batteries

Join a dynamic and collaborative research group with international partnerships in the UK, France, Australia, the US, and Japan.

Award details

Join a dynamic and collaborative research group with international partnerships in the UK, France, Australia, the US, and Japan.

Our team integrates theoretical simulations with experimental research to drive real-world innovation in energy and environmental technologies.

This project focuses on developing advanced computational tools using machine-learning molecular simulations to study fundamental electrochemical processes in sodium-ion batteries. Carbon materials, such as nanoporous carbons and 3D-graphene networks, play a crucial role as electrodes in these batteries, offering high surface area and tunable porosity.

As a PhD researcher, you will work on High-Performance Computing (HPC) systems to create large-scale, realistic models of these materials, investigating key mechanisms like ion storage and electrolyte effects. You will optimize machine-learning interatomic potentials and collaborate with experimentalists to validate your findings. Opportunities to travel to national and international scientific meetings and to teach with full support and mentoring are also available, along with additional compensation.

About our group:

We are a dynamic team of researchers, driven by curiosity and a passion for discovery. Our group thrives in a supportive and flexible environment that fosters creativity and innovation. We actively engage in stimulating scientific discussions and collaborate with an extensive network of international partners in the UK, France, Australia, the US and Japan, ensuring a rich exchange of ideas and expertise. By closely integrating theoretical simulations with experimental research, we aim to maximize the real-world impact of our work, pushing the boundaries of science and technology together.

About the project background:

Carbon materials are at the forefront of green energy and gas storage technologies due to their high surface area, tunable porosity, and exceptional stability. From activated carbons to nanotubes and 3D-graphene networks, these materials enable cutting-edge applications such as supercapacitors, batteries, hydrogen storage, and carbon dioxide capture. Notably, carbon materials are widely used as electrodes in ion batteries, including sodium-ion batteries.

The aim of this project is to develop innovative computational tools that leverage machine-learning molecular simulation techniques to uncover fundamental electrochemical processes in sodium-ion batteries and to guide the design of next-generation carbon electrodes.

Your role:

You will gain hands-on experience working with High-Performance Computing (HPC) systems to produce large-scale, realistic models of nanoporous carbon materials used as electrode materials for sodium-ion batteries. During this process, you will learn and apply cutting-edge molecular simulation and visualization tools to investigate key mechanisms, such as ion storage and diffusion within carbon nanopores, as well as the impact of different electrolytes on ion transport. A key focus will be on optimizing and integrating machine-learning interatomic potentials tailored to the materials under study, enabling more accurate and efficient simulations of sodium storage mechanisms. You will also have the opportunity to collaborate closely with experimentalists to validate your simulations and contribute to real-world advancements in energy and environmental technologies.

Additionally, PhD students can have the opportunity to contribute to the department’s teaching activities, with additional payment provided for their work. Comprehensive training and mentoring in teaching and learning within higher education are offered by the faculty to support their development.

Award value

Funding is available for 3.5 years, covering:

• Stipend: £21,237 per year;
• Bench Fees: £1,000 per year;
• Tuition fees: Home and Overseas fees fully covered;
• Other (please outline): Full access to computational resources.

Eligibility criteria

Applicants should have, or expect to have, an integrated Master’s (e.g., MSci) with first-class honours or upper division second-class honours (2:1), or a BSc plus Master’s (MSc) degree with Merit or Distinction in Physics, Chemistry, Chemical Engineering, Materials Science, Applied Mathematics, Computer Science or related subjects. Equivalent international degrees are equally accepted.

The funding is not restricted to specific nationalities.

The successful applicant will demonstrate strong interest and motivation in the subject, ability to think critically and creatively and strong computational skills. Previous research experience in related subjects and or an interdisciplinary research environment is desirable.

Interested candidates are invited to contact the supervisor (Carla de Tomas, carla.de_tomas@kcl.ac.uk) with a transcript, CV, and motivation letter expressing interest in the project. Informal enquiries are strongly encouraged.

Application process

 To be considered for the position candidates must apply via King’s Apply online application system. Details are available here.

Please apply for [Physics] and indicate Carla de Tomas as the supervisor and quote the project title in your application and all correspondence.

Please ensure to add the following code [StartUpDeTomas] in the Funding section of the application form. Please select option 5 ‘I am applying for a funding award or scholarship administered by King’s College London’ and type the code into the ‘Award Scheme Code or Name’ box. Please copy and paste the code exactly.

The selection process will involve a pre-selection on documents and, if selected, will be followed by an invitation to an interview. If successful at the interview, an offer will be provided in due course.

Contact:

If you require support with the application process please contact physics-pgr@kcl.ac.uk.

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