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AI3SD Video: Data-driven materials discovery for functional applications

AI3SD Video: Data-driven materials discovery for functional applications
AI3SD Video: Data-driven materials discovery for functional applications
Large-scale data-mining workflows are increasingly able to predict successfully new chemicals that possess a targeted functionality. The success of such materials discovery approaches is nonetheless contingent upon having the right data source to mine, adequate supercomputing facilities and machine-learning workflows to calculate or sample a large range of materials, and algorithms that suitably encode structure-function relationships as datamining workflows which progressively short list data toward the prediction of a lead material for experimental validation. This talk shows how to meet these data-science requirements via 'chemistry-aware' natural language processing, image recognition and machine learning developments using case studies to showcase their successful application to data-driven materials discovery.
AI, AI3SD Event, Artificial Intelligence, Chemistry, COVID-19, Datasets, Machine Intelligence, Machine Learning, Materials Discovery, ML, Outliers, Research, Scientific Discovery
Cole, Jacqui
577f4ae8-1ee3-4a31-8aa7-27c12e550c6e
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
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Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Cole, Jacqui
577f4ae8-1ee3-4a31-8aa7-27c12e550c6e
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

Cole, Jacqui (2021) AI3SD Video: Data-driven materials discovery for functional applications. Kanza, Samantha, Frey, Jeremy G., Niranjan, Mahesan and Hooper, Victoria (eds.) AI3SD Winter Seminar Series, , Online. 18 Nov 2020 - 21 Apr 2021 . (doi:10.5258/SOTON/P0082).

Record type: Conference or Workshop Item (Other)

Abstract

Large-scale data-mining workflows are increasingly able to predict successfully new chemicals that possess a targeted functionality. The success of such materials discovery approaches is nonetheless contingent upon having the right data source to mine, adequate supercomputing facilities and machine-learning workflows to calculate or sample a large range of materials, and algorithms that suitably encode structure-function relationships as datamining workflows which progressively short list data toward the prediction of a lead material for experimental validation. This talk shows how to meet these data-science requirements via 'chemistry-aware' natural language processing, image recognition and machine learning developments using case studies to showcase their successful application to data-driven materials discovery.

Video
AI3SD-Winter-Seminar-Series-ML-JacquiCole-Final - Version of Record
Available under License Creative Commons Attribution.
Download (607MB)

More information

Published date: 20 January 2021
Additional Information: Professor Jacqueline Cole holds the Royal Academy of Engineering Research Professorship in Materials Physics at the University of Cambridge, where she is Head of Molecular Engineering. She concurrently holds the BASF / Royal Academy of Engineering Research Chair in Data-driven Molecular Engineering of Functional Materials. This is partly funded by the ISIS neutron and Muon Source, STFC Rutherford Appleton Laboratory, Oxfordshire, UK, with whom she holds a joint appointment. At Cambridge, she carries a joint appointment between the Physics Department (Cavendish Laboratory) and the Department of Chemical Engineering and Biotechnology at Cambridge. She combines artificial intelligence with data science, computational methods and experimental research to afford a 'design-to-device' pipeline for data-driven materials discovery. Her research is highly interdisciplinary. Accordingly, she holds two PhDs: one in Physics from the University of Cambridge and one in Chemistry from the University of Durham.
Venue - Dates: AI3SD Winter Seminar Series, , Online, 2020-11-18 - 2021-04-21
Keywords: AI, AI3SD Event, Artificial Intelligence, Chemistry, COVID-19, Datasets, Machine Intelligence, Machine Learning, Materials Discovery, ML, Outliers, Research, Scientific Discovery

Identifiers

Local EPrints ID: 448778
URI: http://eprints.soton.ac.uk/id/eprint/448778
PURE UUID: dc374771-28b0-4a76-844f-1f77d82bf93c
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: 05 May 2021 16:43
Last modified: 06 May 2021 01:59

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Contributors

Author: Jacqui Cole
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan
Editor: Victoria Hooper

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