Supervisors: Prof. David O. Scanlon (UCL), Prof. Kedar Hippalgaonkar (IMRE, A*STAR, Singapore)
Application deadline: 15/04/2022
Interview date: 29/04/2022
Start date: 26 September 2022
Location: London (1.5 years), Singapore (2 years)
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 Materials Research and Engineering (IMRE) 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.
To combine state of the art computational chemistry techniques in combination with machine learning models to predict the dopability of materials
Defects in crystalline solids introduce local and extended imperfections that define their applicability for most technological applications. For example, the ability to have insulating (non-conducting) behaviour is vital for topological insulators or γ-ray detectors, whereas the ability to dope a material to near metallic levels of conductivity is vital for transparent electrodes in optoelectronic devices. In order to predict the functional properties of crystalline materials, one must therefore understand the effect of defects in such solids. Especially important is the so-called “dopability” of crystals – namely, the ability to predict which dopant atoms can occupy which positions in a crystalline solid and introduce charge carriers to enable functionality. In the era of data-driven research, the ability to screen for materials and compositions that, when doped, give a desired performance, is still an important, unsolved problem.
In this project we will use computational techniques to understand the defect chemistry or a range of materials in the Materials Theory Group (www.davidscanlon.com) at UCL , and our computational predictions will feed into the Machine Learning models developed in the Accelerated Materials Development for Manufacturing Programme at IMRE A*STAR led by Professor Hippalgaonkar (https://kedarh.wixsite.com/nanotransport)
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 Computational Chemistry/Physics/Materials Science and/or coding and/or Machine learning is highly desirable but not necessary as training will be provided.
Interested candidates should initially contact the supervisors, Professor David Scanlon ([Email Address Removed]) and Professor Kedar Hippalgaonkar ([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.