Quantifying the CVD-grown two-dimensional materials <i>via</i> image clustering
Zebin Li, Jihea Lee, Fei Yao, Hongyue Sun
Abstract
The high matching rate between the clustering results and material experts' labels indicated a good accuracy of the proposed clustering algorithm. The proposed unsupervised ML methodology will provide materials scientists with an effective tool kit for efficient evaluation of CVD-grown materials' quality and has a broad applicability for various material systems.
Topics & Concepts
Cluster analysisChemical vapor depositionMaterials scienceComputer scienceImage (mathematics)Unsupervised learningArtificial intelligenceQuality (philosophy)Pattern recognition (psychology)NanotechnologyPhysicsQuantum mechanicsGraphene research and applicationsMachine Learning in Materials ScienceAdvanced Neural Network Applications