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Explainable machine learning in materials science
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DOI：10.1038/s41524-022-00884-7.png)
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Abstract
En 中文
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.
Keywords:
CONVOLUTIONAL NEURAL-NETWORKS
IONIC-CONDUCTIVITY
MICROSTRUCTURE
CLASSIFICATION
EXTRACTION
DISCOVERY
LINKAGES
ALLOYS
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11.9
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2.4K
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