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AI3SD Video: Interpretable machine learning for materials design and characterization

AI3SD Video: Interpretable machine learning for materials design and characterization
AI3SD Video: Interpretable machine learning for materials design and characterization
In a plenary lecture at a recent international conference, one leading researcher in theoretical chemistry remarked "at least 50% of the machine learning papers I see regarding electronic structure theory are junk, and do not meet the minimal standards of scientific publication", specifically referring to the lack of insight in many publications applying ML in that field. But is knowledge inevitably lost in machine learning studies, if not how can it be extracted and how does this apply to machine learning in the context of materials science? In this talk I will look at how we can open up black box machine learning models, to understand the results and gain confidence in predictions. I will present topical examples from designing new dielectric crystals, understanding inelastic neutron scattering data and trusting deep neural networks for tomographic reconstruction. By understanding how and why these models work, we can trust the results and even discover new physical relationships.
AI, AI3SD Event, Artificial Intelligence, Chemical Tomography, Chemistry, Machine Intelligence, Machine Learning, ML, Molecules Discovery, Prediction, Scientific Discovery
Butler, Keith Tobias
f28d27ee-757c-4ddd-be9e-6c930cdb42bb
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
Butler, Keith Tobias
f28d27ee-757c-4ddd-be9e-6c930cdb42bb
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

Butler, Keith Tobias (2020) AI3SD Video: Interpretable machine learning for materials design and characterization. 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/P0084).

Record type: Conference or Workshop Item (Other)

Abstract

In a plenary lecture at a recent international conference, one leading researcher in theoretical chemistry remarked "at least 50% of the machine learning papers I see regarding electronic structure theory are junk, and do not meet the minimal standards of scientific publication", specifically referring to the lack of insight in many publications applying ML in that field. But is knowledge inevitably lost in machine learning studies, if not how can it be extracted and how does this apply to machine learning in the context of materials science? In this talk I will look at how we can open up black box machine learning models, to understand the results and gain confidence in predictions. I will present topical examples from designing new dielectric crystals, understanding inelastic neutron scattering data and trusting deep neural networks for tomographic reconstruction. By understanding how and why these models work, we can trust the results and even discover new physical relationships.

Video
AI3SD-Winter-Seminar-Series-Experiments-KeithButler - Version of Record
Available under License Creative Commons Attribution.
Download (700MB)

More information

Published date: 16 December 2020
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: AI3SD Winter Seminar Series, , Online, 2020-11-18 - 2021-04-21
Keywords: AI, AI3SD Event, Artificial Intelligence, Chemical Tomography, Chemistry, Machine Intelligence, Machine Learning, ML, Molecules Discovery, Prediction, Scientific Discovery

Identifiers

Local EPrints ID: 448780
URI: http://eprints.soton.ac.uk/id/eprint/448780
PURE UUID: 1ecf76e4-27e5-40d1-b4a1-77141616d296
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 16:47
Last modified: 06 May 2021 01:59

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Contributors

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

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