Defects and doping in metal-organic frameworks
Metal-organic frameworks (MOFs) are a class of solid-state porous materials composed of inorganic clusters spatially separated by organic ligands.
The combination of compositional and topological diversity enables unprecedented access to physical properties only arising from the unique MOF composition-function relationship.
As a result these hybrid materials have garnered increased interest over the past two decades due to their potential applications in gas storage and separation, catalysis, sensors, and as battery materials.
Like most semiconductors, the bulk material derives its properties from defects. These defects come in many forms ranging from local redox events to long range diminished crystallographic order.
Essentially every member of the group is working to better understand the emergence and properties of defects in MOFs.
Electron energies and their applications in energy storage and catalysis
The ionization potential/workfunction and electron affinity are two instructive materials properties that essentially determine all chemical reactivity.
Predictions of reactivity can be made with knowledge of the alignment of valence/occupied and conduction/unoccupied orbitals. These values are readily obtained from quantum chemical simulations.
Three general types of band alignments are possible; Type I where one material's bands sit mid gap relative to a neighbouring material, Type II where charge transfer from one material to another is enabled by the input of some form of energy, and Type III where a material will spontaneously reduce another.
Regressions and machine learning
Computers are extremely good at finding dependencies and relationships in data. In order to to so, we usually first teach the computer some simple concepts, and then expose it to uncharted territory to begin to make predictions.
Despite these techniques existing for 50 years, the application of these techniques chemistry is only fairly recent.
Using a mixture of off-the-shelf and proprietary machine learning models we are able to predict chemical properties of small molecules, MOFs, and even bridge the difficult gap between human perception of a property and the underlaying chemistry that gave rise to our perception of that property.