Post-doctoral project

While porous carbons are used in many applications including energy storage, gas storage, water treatment, and catalysis, they are still poorly characterised due to their disordered nature and complex porosity.

In this project, we propose to combine a quantum chemical and machine learning based approaches to predict magnetic properties of such carbons. This original approach will allow us to gain insights into Nuclear Magnetic Resonance (NMR) experimental results, a technique which has already proven its usefulness in the study of such systems, allowing for the quantification of adsorbed species inside the carbon or the estimation of pore size distributions. In addition to these properties, in principle NMR data provides information about the local structure and defects. Current theoretical models are limited to small aromatic molecules, the link between microscopic structure and macroscopic measurements is missing.

The model we propose to build will be suitable for periodic solids as well as small aromatic molecules. We will be able to gain a deeper understanding of the relationship between structural defects and electronic/magnetic properties as well as improving the characterisation of disordered porous carbons, an essential step in order to boost their performance in various applications.

This postdoctoral project will benefit from the expertise of Chris Pickard (University of Cambridge) and Albert Bartók (Science and Technology Facilities Council), pioneers in structure and NMR parameters prediction as well as development of machine-learning methods, and of Céline Merlet (Université Toulouse 3 - CNRS), specialised in studying carbon-based energy storage systems through molecular and coarse-grained simulations.

Contact: Céline Merlet, [email protected]

Deadline for applications: 15th of June 2018

More information is available here.