Rapid Materials Discovery by Automated Machine Learning

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Porous materials have a wide spectrum of important industrial applications. Recent developments in materials chemistry have shown that metal-organic frameworks (MOFs) have promising properties that complement or compete favourably with zeolites and activated carbons in various applications. MOFs are crystalline porous coordination polymers consisting of polyatomic organic ligands linked to metal ions/clusters by covalent bonds. MOFs have shown great promise for gas adsorption and storage owing to their high porosity and internal surface area, and tuneable functionality on the pore surface for selective gas binding. Generation of open metal sites and incorporation of pendant functional groups at the pore surface are two dominant methods of functionalising MOF cavities. Given an unlimited combination of metal ions and organic ligands, MOFs have an ultra-high degree of structure design flexibility and tuneability; over 6000 new MOFs in the past 10 years have been added to the CCDC database. We have recently discovered a family of very stable MOF materials (MFM-300 series) incorporating isophthalate linkers. MFM-300(Al) exhibits excellent performance in selective carbon capture and hydrocarbon separations. More recently, we reported the first example of studies on guest binding in a pair of isostructural redox-active MOFs. However, much of this discovery was empirical. The best route to predictive chemistry is via the automated (machine) learning of paired physical property-function relationships from large datasets. This PhD project will establish a new approach to the development of new MOF materials via state of the art Predictive Deep (machine) Learning (the technology behind Apple's Siri and Google driverless cars), encoded in KNIME (e.g. O'Hagan S, Kell DB: The KNIME workflow environment and its applications in Genetic Programming and machine learning. Genetic Progr Evol Mach 2015; 16:387-391). Predictive models suggest the materials to build and test, iteratively, and allow one to navigate the eeffective 'landscape' intelligently (see Currin et al. Chem Soc Rev 2015; 44:1172-1239). This mixed wet/dry project combines the expertise of Yang (on MOF synthesis and characterisation) and Kell (on Design of Experiments and machine learning) and is thus inter-disciplinary and transformative. We anticipate that a large family of new MOFs will be prepared at the end of this project, showing various exciting materials properties.