About the Project
Supervisor: Prof. Graeme Day
Project description
Materials impact most aspects of our lives, including healthcare, energy production, data storage and pollution control. However, the discovery of new advanced materials is often approached by trial-and-error experimentation. This project will work on the development of computational methods that can guide materials discovery through the interpretation of crystal structure landscapes that are predicted using computational methods for crystal structure prediction (CSP).
Our group has pioneered the use of CSP for the discovery of functional materials in areas such as porous materials (Nature, 2017, 543, 657) and organic semiconductors (J. Mater. Chem. C, 2017,5, 7574-7584, Chem. Sci., 2018,9, 1289-1300). One of the challenges in this work is the over-prediction of crystal structures: CSP methods tend to predict many more crystal structures than are ever observed experimentally. This project will develop advanced methods for identifying the most likely synthesisable crystal structures from CSP studies, using machine learning approaches for the analysis of computer-generated structural landscapes. The project will implement and validate the use of the generalised convex hull (Phys. Rev. Materials, 2018, 2, 103804) for the identification of synthesisable structures; this is a robust, data-driven approach to find "extremal" structures on energy landscapes that can be stabilised by application of some experimental constraint, eg. fields, doping, interaction with solvent or chemical modification of the constituent molecule.
The project is based in the computational materials discovery research group led by Prof. Graeme Day at the University of Southampton and forms part of a collaboration with Prof. Michele Ceriotti (Ecole Polytechnique Federale de Lausanne), who will co-supervise the student. The student will be part of the Leverhulme Research Centre for Functional Materials Design (https://www.liverpool.ac.uk/leverhulme-research-centre) and will interact with other projects within the Centre, such as for experimental verification of the computational methods.
The project is fully funded for 3.5 years, including fees, a maintenance grant and funds for travel. Fees are covered at the rate of UK/EU students. Experience with computational chemistry and/or programming is an advantage.
Applicants do not need to have previous experience with crystal structure prediction, but should have a good degree (equivalent to a UK first or upper second class) in chemistry, materials science or a related discipline, and an aptitude for research. Applicants should thrive in a collaborative environment and expect to work closely with other computational chemists in the research group, as well as collaborators in both computational and lab-based environments.
Entry Requirements
A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).
Closing date: applications should be received no later than 31 August 2020 for standard admissions, but later applications may be considered depending on the funds remaining in place.
Funding: full tuition fees for EU/UK students plus for UK students, an enhanced stipend of £15,285 tax-free per annum for up to 3.5 years.
How To Apply
Applications should be made online, please select the academic session 2020-21 “PhD Chemistry (Full time)” as the programme. Please enter Graeme Day under the proposed supervisor.
Applications should include:
Curriculum Vitae
Two reference letters
Degree Transcripts to date
Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page
For further information please contact: [Email Address Removed]
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