About Us

We are an academic research group focussed on discovering new solid-state materials with useful properties including magnetism, electronic/ionic conduction and phase transitions. We combine experimental synthesis, physical characterisation and data-driven (machine learning) approaches. Our current interests include mixed anion materials and the relationship between atomic structure and physical properties.


Mixed anion control of negative thermal expansion in a niobium oxyfluoride

Graphic showing how changing the amount of fluorine results in larger structural vibrations

Although most materials expand when heated, a small minority actually contract or retain the same volume over wide temperature ranges. These unusual materials can help to overcome many of the engineering challenges caused by thermal expansion in, for example, fuel cells or optical components. We have identified a new approach for controlling thermal expansion through fluoride doping of niobium oxyfluorides. A small fluoride content increase leads to zero and then negative thermal expansion. To our knowledge this is the first instance of negative thermal expansion in an oxyfluoride.

Eliza K. Dempsey and James Cumby, Mixed anion control of negative thermal expansion in a niobium oxyfluoride, Chemical Communications, 2024, Advance Article

Grouped Representation of Interatomic Distances (GRID)

Schematic of ordered pairwise distances being combined with composition and earth mover's distance to quantify similarity between materials.

If two materials share the same atomic species in the same positions, they will exhibit the same phyiscal properties. Extending this idea further, quantifying how similar different materials are would allow us to predict their properties, given enough data to compare. Machine learning (ML) models can be used to make these predictions, but unfortunately crystal structures are not well-suited to existing ML methods. 

Here, we have developed a "grouped representation of interatomic distances" (GRID) as a way to feed crystallographic structure to machine learning algorithms. Although simple, when combined with Earth mover's distance this representation has the ability to accurately predict physical properties of materials, and reflects chemical intuition. Importantly, GRID can be applied to both crystalline and disordered materials without modification.

R.-Z..Zhang, S. Seth and J. Cumby, Grouped representation of interatomic distances as a similarity measure for crystal structures, Digital Discovery, 2, 2023, 81-90.

Controlling electronic properties in oxyfluorides

Image showing how both pressure and fluoride doping cause half-metallic behaviour in niobium oxyfluorides

Half-metallic materials have the unique electronic property of being conducting in one electron spin direction whilst insulating in the other. They have the potential to vastly improve data processing and storage technology however current half-metallic materials do not meet the standards required. Through computational studies we have identified a new approach for controlling half-metallicity through pressure and chemical composition. The niobium oxyfluoride system is predicted to switch from metallic to half-metallic under applied pressure. The electronic properties can also be controlled by changing the ratio of oxygen and fluorine.

E. K. Dempsey and J. Cumby, Metallic to half-metallic transition driven by pressure and anion composition in niobium oxyfluoride, Journal of Materials Chemistry C, 11, 2023, 1791-1797.

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