Supervisors: Prof. Ben Slater (UCL), Dr. Jia Zhang (IHPC, A*STAR, Singapore)
Application deadline: 15/04/2022
Interview date: TBC (2 to 4 weeks after the application close date)
Start date: 26 September 2022
Location: London (1.5 years), Singapore (2 years)
Subject areas: Catalysts, Chemical modification, High Performance Computing, Machine Learning
This position is fully funded by the UCL-A*STAR (Agency for Science, Technology and Research, Singapore) Collaborative Programme via the Centre for Doctoral Training in Molecular Modelling and Materials Science (M3S CDT) at UCL. The student will be registered for a PhD at UCL where he/she will spend year 1 and the first six months of year 4. The second and third years of the PhD will be spent in the Institute of High Performance Computing (IHPC) of A*STAR in Singapore. The studentship will cover tuition fees at the home rate, and an annual stipend of no less than £17,609 increasingly annually with inflation (tax free) pro rata in years 1 and 4. During years 2 and 3, the student will receive a full stipend directly from A*STAR. In addition, A*STAR will provide the student a one-off relocation allowance.
Prof Ben Slater of UCL (Chemistry), UK and Dr Jia Zhang of IHPC (A*STAR), Singapore are launching an interdisciplinary, combined theory and experimental study into identifying the best performing metal nanoparticle catalyst within selected zeolites for transforming CO2 into useful chemical feedstocks such as methanol, formic acid and ethanol. The project will benefit from experimental support from Prof Ning Yan at NUS, a world renowned expert on developing catalysts for upscaling small molecules. The appointed student will spend the initial phase of the project performing fundamental computer simulations to examine, at the molecular level, the transformation mechanism of CO2 to methanol on metal nanoparticles. Next, the student will assess (via screening) how doping with metals such as Pd affects the energetics and barriers in the transformation. This step will seek to identify descriptors so that machine learning protocols can be developed and applied to accelerate the identification of the most promising functional metal nanoparticle. The final step will investigate where the nanoparticles sit within the zeolites of interest (e.g. zeolite Y, zeolite A and ZSM-5) and how the reaction profiles for CO2 upscaling are affected. The most promising predicted materials will be investigated by Prof Yan’s group and the experimental results cross-referenced with the predictions to establish a feedback loop to refine the machine learning and high-throughput aspects of the research.
The applicants should have, or be expecting to achieve, a first or upper second-class integrated masters degree (MSci, MChem, etc.) or 2:1 minimum BSc plus stand-alone Masters degree with at least a Merit in Chemistry or materials science. The successful applicant will demonstrate strong interest and self-motivation in the subject, good experimental practice and the ability to think analytically and creatively. Good computer skills, plus good presentation and writing skills in English, are required. Previous research experience in contributing to a collaborative interdisciplinary research environment is highly desirable but not necessary as training will be provided.
Interested candidates should initially contact the supervisors (Prof Ben Slater [Email Address Removed] and Dr Jia Zhang [Email Address Removed]) with a degree transcript and a motivation letter expressing interest in the project. Informal inquiries are encouraged.
Please note that a suitable applicant will first be required to complete MS Form entitled Application for Research: degree Chemistry programme. The next step is to complete an electronic application form at http://www.ucl.ac.uk/prospective-students/graduate/apply (please select Research degree: Chemistry programme).
All shortlisted applicants will be invited for the interview no more than 4 weeks after the application deadline.
Any admissions queries should be directed to Dr Zhimei Du [Email Address Removed]
Applications will be accepted until 15/04/2022.