Interpretable and Explainable Machine Learning for Materials Science and Chemistry-Reference-Cited by-同舟云学术

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Interpretable and Explainable Machine Learning for Materials Science and Chemistry

Published:2022-06-03 Issue:6 Volume:3 Page:597-607
ISSN:2643-6728
Container-title:Accounts of Materials Research
language:en
Short-container-title:Acc. Mater. Res.

Author:

Oviedo Felipe¹²,Ferres Juan Lavista²,Buonassisi Tonio¹^ORCID,Butler Keith T.³⁴^ORCID

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

2. Microsoft AI for Good Research Lab, Redmond, Washington 98052, United States

3. SciML, Scientific Computing Department, Rutherford Appleton Laboratory, Didcot OX110D, U.K.

4. Department of Chemistry, University of Reading, Reading RG6 6AD, U.K.

Funder

Microsoft

Singapore-MIT Alliance for Research and Technology Centre

National Research Foundation

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Polymers and Plastics,Materials Science (miscellaneous),Chemical Engineering (miscellaneous)

Link

https://pubs.acs.org/doi/pdf/10.1021/accountsmr.1c00244

Reference49 articles.

1. The Mythos of Model Interpretability

2. Explaining Explanations: An Overview of Interpretability of Machine Learning

3. Explanation in artificial intelligence: Insights from the social sciences

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