Development of a meta-heuristic algorithm for MOF crystal structure prediction

Overview

Functional porous materials based on two- and three-dimensional coordination polymers are conceptually the result of a fantastic Lego game. Indeed, their crystalline framework is the result of the arrangement of elementary components: a metal site, associated with a (redox) ligand, and a counter-ion inserted into the cavities of the metal-ligand framework. The intimate association of these three components metal, ligand and counter-ions, in a key-lock type game, modulates the material's physico-chemical properties: crystallinity, network topology, conducting behaviour, magnetic order, catalytic activity, etc. Exploring the possibilities of such an ensemble by usual methods, whether experimental or theoretical, is dizzying. A change of paradigm is thus required.

In this joint project between Nottingham Trent University and Université de Poitiers, France, which will lead to a Dual Award PhD, we propose to elaborate a methodology to design and develop Functional Metal Organic Frameworks (MOFs) by an intelligent exploration of the whole set of possibilities issuing from combinations of metal/ligand/counter ions. Along with Prof Gilles Frapper (Université de Poitiers, France) we will extend and integrate the AuToGraFS structure builder with the XTalOpt evolutionary algorithm and machine-learned potentials to build a fully open-source multi-component MOF structure search platform. Then, we will focus on the prediction of new MOFs with designed properties thanks to selected fitness functions (band gap, thermoelectric, magnetism...).