People

Ellie started her PhD in September 2021, following completing her undergraduate Chemical Physics project in 2020-21. She is particularly interested in materials science and the intersections between Chemistry and Physics. Her research if focusses on understanding electronic and structural properties of niobium oxyfluorides using a mixed computational and lab-based approach. She is aiming to determine how altering their composition and short-range structure impacts their physical and mechanical properties.

Jonny is a current chemistry undergraduate student who has joined the Cumby group as part of his integrated masters degree. Throughout his degree so far, Jonny has always had a keen interest in materials science and so opted for a project this year that would satisfy this. His work will centre around applying computational techniques to a differing oxygen/fluorine ratio within a niobium-focussed oxyfluoride material in an effort to investigate the collected data via computational analysis to determine effects on the material's negative thermal expansion (NTE). Along with NTE, Jonny is looking for how the composition of the niobium oxyfluoride affects its structural and geometric properties, and how this can then be implemented in everyday applications.  

Marta is a final year Chemistry (MChem) student with a particular interest in physical chemistry and computer science. Her summer project is funded by the Royal Society of Chemistry Undergraduate Bursary and explores a novel method of describing crystal structures based on interatomic distances, and its applicability to machine learning models predicting new oxyfluoride materials.

Ruizhi joined the Cumby Group as a postdoctoral researcher, financially supported by an AI3SD project. Before this post, he worked at Queen Mary University of London and Northwest University(Xi'an). He holds a PhD in condensed matter physics. In the Cumby Group, his research mainly focuses on developing new descriptors for machine learning algorithms, which can be used for properties prediction of periodic structures, i.e. solid-state materials. His research aims to develop novel descriptors that contain both short-range and long-range information of the crystal structure, and use it to predict materials properties and provide some guidance for experimental synthesis. 

Scott completed his BSc in Chemistry at the University of Aberdeen in June 2019.  He became the first member of the Cumby group in September 2019.  His research focus is the synthesis of new solid-state transition metal oxyfluorides as well as the characterisation of their magnetic, electronic and ionic properties.  Scott is also interested in Magnéli phase materials and the effects of local ordering of anions on physical properties.

With a Bachelor degree in Chemistry from the University of Liverpool, Edmund joined the Cumby Group for his Materials Chemistry Masters project at the University of Edinburgh. Edmund's Masters project is focused on deeper understanding of the structures of wolframite and using automation to discover further possible wolframite materials in the ISCD database. By developing new techniques to automate the comparison of solid-state structures, a more comprehensive view of wolframites can be taken. This can act as a template for the understanding of other key solid-state structures. The ultimate goals are to apply automation to facilitate the comparison of large numbers of structures; to allow machine-learning techniques to be employed in these comparisons;  to understand the structure-property relationships and to direct materials development.

Chenyu completed his undergraduate in Materials Science and Engineering at the Beijing University of Chemical Technology in July 2019. Currently, he is a masters student in Computer Science at the University of Edinburgh.  Chenyu is interested in deep learning, information retrieval, recommender systems as well as the application of machine learning techniques in the field of Chemistry and Materials. His master's degree project supervised by Drs James Cumby and Sohan Seth is to investigate the methods of positive unlabelled learning to predict whether metal oxides are susceptible to fluorination.