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Machine learning for 2D material–based devices
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DOI：10.1016/j.mser.2025.101085.png)
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Abstract
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• Machine learning significantly enhances the efficiency and quality of 2D material synthesis. • Integrating machine learning with characterization techniques improves accuracy in defect and property analysis. • Data-driven models optimize fabrication parameters and predict the performance of 2D devices. • Machine learning drives interdisciplinary advancements from fundamental research to applications in 2D materials.
Keywords:
machine learning
2D materials
synthesis
characterization
data-driven models
Journal
M
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26.8
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809
Citations:
1.1W
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