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Domain knowledge-assisted materials data anomaly detection towards constructing high-performance machine learning models
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DOI：10.1016/j.jmat.2025.101066.png)
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Abstract
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• A domain knowledge-assisted data anomaly detection (DKA-DAD) workflow is first proposed. • Domain knowledge is symbolized and embedded into the 3 designed detection models for evaluation from different dimensions. • DKA-DAD governs 60 collected materials datasets with an average 9 % insight improvement, outperforming existing methods.
Keywords:
Machine learning
Materials science
Data anomaly
Domain knowledge
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