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AI3SD Video: Interpretable Machine Learning for Materials’ Design and Characterisation

AI3SD Video: Interpretable Machine Learning for Materials’ Design and Characterisation
AI3SD Video: Interpretable Machine Learning for Materials’ Design and Characterisation
“Where is the knowledge we have lost in information?” T.S. Eliot, The Rock
Machine learning (ML) and artificial intelligence (AI) are the subjects of wildly differing opinions on utility and potential impact. Depending who we talk to ML is the solution to almost every human challenge, from open boarders to pandemic control, or presents an existential crises for the species. In materials science the polarisation is perhaps less extreme, but nonetheless pervasive, while the numbers of ML related works experiences an explosion one highly respected theoretical chemist recently pronounced “[a]t least 50% of the machine learning papers I see regarding electronic structure are junk”. A part of the issue that many detractors have with ML methods is related to their perception of the techniques as ‘black-box’ approaches, at the same time, the same lack of understanding limitations of the models leads to some of the more outlandish boosterism surrounding the subject. In this talk I will discuss, with examples from our work, how we can open up the black-box of ML methods, highlighting and understanding limitations, increasing trust in results, and potentially improving the methods themselves.
Butler, Keith Tobias
f28d27ee-757c-4ddd-be9e-6c930cdb42bb
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
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Butler, Keith Tobias
f28d27ee-757c-4ddd-be9e-6c930cdb42bb
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Butler, Keith Tobias (2022) AI3SD Video: Interpretable Machine Learning for Materials’ Design and Characterisation. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom. 01 - 03 Mar 2022. (doi:10.5258/SOTON/AI3SD0194).

Record type: Conference or Workshop Item (Other)

Abstract

“Where is the knowledge we have lost in information?” T.S. Eliot, The Rock
Machine learning (ML) and artificial intelligence (AI) are the subjects of wildly differing opinions on utility and potential impact. Depending who we talk to ML is the solution to almost every human challenge, from open boarders to pandemic control, or presents an existential crises for the species. In materials science the polarisation is perhaps less extreme, but nonetheless pervasive, while the numbers of ML related works experiences an explosion one highly respected theoretical chemist recently pronounced “[a]t least 50% of the machine learning papers I see regarding electronic structure are junk”. A part of the issue that many detractors have with ML methods is related to their perception of the techniques as ‘black-box’ approaches, at the same time, the same lack of understanding limitations of the models leads to some of the more outlandish boosterism surrounding the subject. In this talk I will discuss, with examples from our work, how we can open up the black-box of ML methods, highlighting and understanding limitations, increasing trust in results, and potentially improving the methods themselves.

Video
ai4sd_march_2022_day_2_KeithButler - Version of Record
Available under License Creative Commons Attribution.
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More information

Published date: 2 March 2022
Additional Information: Keith Butler is as a senior data scientist working on materials science research in the SciML team at Rutherford Appleton Laboratory. SciML is a team in the Scientific Computing Division and we work with the large STFC facilities (Diamond, ISIS Neutron and Muon Source and Central Laser Facility for example) to use machine learning to push the boundaries of fundamental science.
Venue - Dates: AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom, 2022-03-01 - 2022-03-03

Identifiers

Local EPrints ID: 468639
URI: http://eprints.soton.ac.uk/id/eprint/468639
PURE UUID: 25a82d0b-3db9-44b7-b98e-83fcecff22db
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 19 Aug 2022 16:34
Last modified: 20 Aug 2022 01:54

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Contributors

Author: Keith Tobias Butler
Editor: Jeremy G. Frey ORCID iD
Editor: Samantha Kanza ORCID iD
Editor: Mahesan Niranjan ORCID iD

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