About the Project
The vision of the project is to transform materials discovery into a routine computational task. Materials discovery still needs a lot of human expertise, trial-and-error and even pure luck, because there is no rigorous theory to guide an efficient search in the huge space of all theoretically possible materials. Any solid crystalline material (briefly, a crystal) will be modeled as a periodic structure formed by repeated patterns of molecules, atoms or ions.
The practical aim is to substantially accelerate the in silico crystal design pioneered in the Nature paper  by Dr Pulido and Prof Cooper, the director of the Materials Innovation Factory.
The real data will come from the Cambridge Structural Database (CSD), which now contains more than 1M known crystals and is curated by Dr Pulido and Dr Cole at the Cambridge Crystallographic Data Centre (CCDC).
The student will implement efficient software to compute new geometric invariants developed in Dr Kurlin’s group for comparing crystal packings. These continuous invariants will allow us to visualise any dataset of simulated crystals to select most promising candidates for synthesising in state-of-the-art robotic lab at the Materials Innovation Factory.
The computed invariants will allow us to visualise geometric similarities in any dataset of simulated crystals that are currently represented only by energy-vs-density plots of isolated dots . The ultimate goal is to build a navigational map for the whole CSD to guide an optimal search for new crystals instead of the traditional random sampling.
Applications are welcomed from students with a 2:1 or higher masters degree or equivalent in Computer Science or Computational Chemistry or related areas. Strong communication skills and programming experience in C++ or Python are essential. Informal enquiries can be e-mailed to Dr Vitaliy Kurlin, [Email Address Removed].
Please apply by completing the online postgraduate research application form. Please ensure you quote the following reference on your application: LRC1914 - Towards prediction of synthetically accessible organic molecular crystals. Please note that this project will remain open until a successful candidate is found, hence apply early to avoid disappointment.
The award is primarily available to students resident in the UK/EU and will pay full tuition fees and a maintenance grant for 3 years (£15,009 per year in 2019-2020). Applications from non-EU candidates can be considered if you can cover the difference (about £17.5K per year) between international and UK/EU tuition fees. This studentship is 50% funded by the Cambridge Crystallographic Data Centre.
 Pulido, Angeles, Linjiang Chen, Tomasz Kaczorowski, Daniel Holden, Marc A. Little, Samantha Y. Chong, Benjamin J. Slater et al. Functional materials discovery using energy–structure–function maps. Nature 543, no. 7647 (2017): 657.
 Musil, Félix, Sandip De, Jack Yang, Joshua E. Campbell, Graeme M. Day, and Michele Ceriotti. Machine learning for the structure–energy–property landscapes of molecular crystals. Chemical science 9, no. 5 (2018): 1289-1300.