Statistical Crystal Structure Prediction and Modelling

Chemistry/Statistics/Engineering Sciences

There is an opportunity for a four-year PhD studentship to research the prediction of crystal structures using statistical models in a multi-disciplinary team, based at the Institute for Complex Systems Simulation – a Doctoral Training Centre at the University of Southampton (http://www.icss.soton.ac.uk/).

Materials properties are invariably uniquely defined by the atomic structure. Structure-property relationships are, therefore, of intense interest in materials science and engineering. The ability to predict the structure of materials would open the door to true virtual materials design. But the complexity of the task, strikingly demonstrated by the myriad of structures Nature adopts, prevents direct prediction from First Principles alone. A viable approach to structure prediction takes advantage of known information by capturing the complex many-body physics that drive structure formation in a statistical sense. Indeed, it is becoming increasingly important to be able to statistically model the structure of materials in order to be able to answer fundamental questions about the driving force behind formation, design, modification, and manufacturing scale-up.
Structures can broadly be broken down into different classes (e.g., metals, frameworks, and molecular crystals), which have fundamentally different characteristics. All have important applications, and research concerned with their prediction is one of the hottest multi-disciplinary areas today, linking Chemistry, Materials Science and Engineering, and Applied Math. Southampton has a number of research groups interested in the prediction and statistical modelling of crystal structures, which benefit from an overlap between experimental and theoretical approaches. The special interest group spans three faculties and comprises: Dr’s Coles and Day (Chemistry) investigating organic and pharmaceutical-like structures from experimental and theoretical approaches respectively; Dr Kramer (Engineering) investigating framework materials for electrochemical applications; Prof Reed (Engineering) researching failure in engineering alloys and metals; and Dr Woods (Statistics) researching statistical modelling of materials.
This special interest group is looking to exploit synergies and overlaps between methods being employed and developed in these groups, with a view to developing a toolkit for conducting and managing the application of statistics to the modelling and prediction of crystal structures of any type. This PhD work would initially involve research into the application of statistical approaches to predicting framework structures for use in electrochemical and optoelectronic applications. Work would then progress on to investigate the implementation of statistical tools to describe crystal structures in order to be able to recognize trends and quantitatively answer questions governing the fundamental driving forces behind crystal structure formation. This latter work may then in turn be applied to the development of tools to assist in the ranking of putative crystal structures arising from predictive methods.
The successful applicant has demonstrated his/her passion for Science with an outstanding degree (First or equivalent), enjoys working at the interface between disciplines, is a team-player, and is equally motivated by a desire to unravel natures mysteries and impact society for the better. Strong theoretical and analytical skills as well as aptitude for computational work are a plus.