Explainable machine learning in materials science Article Swipe

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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

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.

Type

article

Language

en

Landing Page

https://doi.org/10.1038/s41524-022-00884-7

PDF

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

Raw OpenAlex JSON