Understanding the importance of individual samples and their effects on materials data using explainable artificial intelligence-Reference-Cited by-同舟云学术

Understanding the importance of individual samples and their effects on materials data using explainable artificial intelligence

Published:2024 Issue:2 Volume:3 Page:422-435
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Liu Tommy¹^ORCID,Tho Zhi Yang²,Barnard Amanda S.¹^ORCID

Affiliation:

1. School of Computing, Australian National University, 145 Science Road, Acton 2601, Canberra, Australia

2. Research School of Finance, Actuarial Studies and Statistics, Australian National University, 26C Kingsley Street, Acton 2601, Canberra, Australia

Abstract

Explaining the influence of data instances (materials) to predictions such as structure/property relationships in materials informatics can complement structural feature importance profiling, and guide data generation, cleaning, and verification.

Funder

National Computational Infrastructure

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00171G

Reference35 articles.

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5. T.Liu and A. S.Barnard , International Conference on Machine Learning, ICML 2023, 23-29 July 2023 , Honolulu, Hawaii, USA , 2023 , pp. 21375–21387

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