The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

AI3SD Video: Quantifying crystal similarity

AI3SD Video: Quantifying crystal similarity
AI3SD Video: Quantifying crystal similarity
This talk forms part of the ML4MC (Machine Learning for Materials and Chemicals Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Directed Assembly Network. This series ran over summer 2021 and covers topics that encompass our overlapping Network interests of AI, Machine Learning, Artificial Photosynthesis, Biomimetic Materials, Crystal Design & Engineering, Materials, Molecules, Photochemistry, Photocatalysis and Supramolecular Chemistry. This video was the eleventh talk in the ML4MC series and formed part of the session "Mentor Talks".
AI3SD Event, Direct Assembly, Materials, Chemicals, Machine Learning, Summer School, Training
Cumby, James
f2d28653-c29c-4a5a-94d8-824aa67c852d
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Cumby, James
f2d28653-c29c-4a5a-94d8-824aa67c852d
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

Cumby, James (2021) AI3SD Video: Quantifying crystal similarity. Kanza, Samantha, Frey, Jeremy G. and Hooper, Victoria (eds.) Machine Learning for Materials & Chemicals Seminar Series 2021, , Southampton, United Kingdom. 06 Jul - 24 Aug 2021. (doi:10.5258/SOTON/P0142).

Record type: Conference or Workshop Item (Other)

Abstract

This talk forms part of the ML4MC (Machine Learning for Materials and Chemicals Series which has been organised as a joint venture between the Artificial Intelligence for Scientific Discovery Network+ (AI3SD) and the Directed Assembly Network. This series ran over summer 2021 and covers topics that encompass our overlapping Network interests of AI, Machine Learning, Artificial Photosynthesis, Biomimetic Materials, Crystal Design & Engineering, Materials, Molecules, Photochemistry, Photocatalysis and Supramolecular Chemistry. This video was the eleventh talk in the ML4MC series and formed part of the session "Mentor Talks".

Video
Ml4MC-JamesCumby-100821 - Version of Record
Available under License Creative Commons Attribution.
Download (368MB)

More information

Published date: 10 August 2021
Additional Information: 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.
Venue - Dates: Machine Learning for Materials & Chemicals Seminar Series 2021, , Southampton, United Kingdom, 2021-07-06 - 2021-08-24
Keywords: AI3SD Event, Direct Assembly, Materials, Chemicals, Machine Learning, Summer School, Training

Identifiers

Local EPrints ID: 450848
URI: http://eprints.soton.ac.uk/id/eprint/450848
PURE UUID: d869aeea-35bf-4091-9063-72d24186846c
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 16 Aug 2021 16:45
Last modified: 17 Aug 2021 01:58

Export record

Altmetrics

Contributors

Author: James Cumby
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Victoria Hooper

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×