Explainable machine learning in materials science Article Swipe
Xiaoting Zhong
,
Brian Gallagher
,
Shusen Liu
,
Bhavya Kailkhura
,
Anna M. Hiszpanski
,
T. Yong-Jin Han
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-022-00884-7
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1038/s41524-022-00884-7
Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41524-022-00884-7
- https://www.nature.com/articles/s41524-022-00884-7.pdf
- OA Status
- gold
- Cited By
- 260
- References
- 132
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4296612997
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4296612997Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41524-022-00884-7Digital Object Identifier
- Title
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Explainable machine learning in materials scienceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-22Full publication date if available
- Authors
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Xiaoting Zhong, Brian Gallagher, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, T. Yong-Jin HanList of authors in order
- Landing page
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https://doi.org/10.1038/s41524-022-00884-7Publisher landing page
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https://www.nature.com/articles/s41524-022-00884-7.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41524-022-00884-7.pdfDirect OA link when available
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Field (mathematics), Artificial intelligence, Computer science, Context (archaeology), Point (geometry), Artificial neural network, Machine learning, Data science, Deep learning, Management science, Engineering, Mathematics, Geometry, Pure mathematics, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
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260Total citation count in OpenAlex
- Citations by year (recent)
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2025: 122, 2024: 82, 2023: 55, 2022: 1Per-year citation counts (last 5 years)
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132Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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