AI3SD Intern Project: Uniting real and reciprocal space with machine learning

Published date: 25 September 2021

Additional Information: Sarah Scripps: I am a Chemical Physics student at the University of Edinburgh starting my fourth year in September. Chemical Physics allows me to study the core concepts from both subjects as well as their intersections in later years. I enjoy the variety in studying both subjects where physics gives a deeper insight into the underlying mathematics and principles, chemistry shows further applications and different ways of viewing the same problems. The University of Edinburgh is one of the few places that offers such an interdisciplinary course and so was the ideal choice. I have a particular interest in theoretical and computational chemistry and hope to go on to do a PhD once I have completed my current studies. James Cumby: James is a lecturer in inorganic chemistry, with a focus on discovering new solid state materials with useful properties such as magnetism, ionic or electronic conductivity, and negative thermal expansion. His research combines experimental syntheses and characterisation with computational methods such as machine learning and electronic structure calculations; atomic structure (both periodic and short-ranging) underpins all of this work. James obtained his PhD in solid state chemistry from the University of Birmingham, and before his current position worked as a postdoctoral researcher at the University of Edinburgh.

Local EPrints ID: 452290

URI: http://eprints.soton.ac.uk/id/eprint/452290

DOI: doi:10.5258/SOTON/AI3SD0149

PURE UUID: 672948ea-e585-424d-9840-2eec3568b364

ORCID for Samantha Kanza: orcid.org/0000-0002-4831-9489

ORCID for Jeremy G. Frey: orcid.org/0000-0003-0842-4302

Date deposited: 03 Dec 2021 17:30

Last modified: 17 Mar 2024 03:51