Supervisory Team : Prof. Graeme Day
A fully funded PhD studentship is available in the area of computational materials discovery, as part of a prestigious international Synergy grant funded by the European Research Council.
The project, Autonomous Discovery of Advanced Materials’, aims to revolutionise the way that new materials are discovered by combining computational simulations, robotics and materials synthesis.
Within this studentship, you will develop computational methods that can guide the discovery of new materials. Computational methods are developing rapidly in this area, with the use of crystal structure prediction to assess molecules for their likely crystal packing and resulting materials properties.
One of the great challenges for these methods is to decide which molecules to assess. The space of all possible molecules is huge, making exhaustive assessment of all possible molecules in this chemical space impossible.
This project will develop methods for efficiently navigating within chemical space to identify new molecules whose crystal packing will lead to promising properties.
We have recently shown that it is possible to use crystal structure prediction for high throughput assessment of molecules (see Chemistry of Materials 2018, 30, 13, 4361 4371), and we have coupled this with evolutionary methods to find the best candidate molecules (see Chemical Science 2020,11, 4922-4933).
You will continue the development of these methods, applying them to a range of materials discovery projects in the areas such as photocatalysis (see Journal of Materials Chemistry A , 2020, 8 , 7158-7170) and porous materials for gas storage or separations (see Nature 2017, 543, 657 664).
The project is based in the computational materials discovery research group led by Prof. Graeme Day (