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RHEED Analysis
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RHEED Analysis
Supporting Literature
Unsupervised Algorithms
AtomCloud uses unsupervised algorithms for our dimensionality reduction and clustering
Principal component analysis (standard implementation)
PacMAP
HDBSCAN
PacMAP
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization (Wang, Et AL.)
HDBSCAN
hdbscan: Hierarchical density based clustering (McInnes, Et AL.)
Clustering
Machine learning analysis of perovskite oxides grown by molecular beam epitaxy (
Sydney, Et Al.)
Engineering ordered arrangements of oxygen vacancies at the surface of superconducting La2CuO4 thin films (Suyolcu, Et Al.)
Skill-Agnostic analysis of reflection high-energy electron diffraction patterns for Si(111) surface superstructures using machine learning (Asako Yoshinari, Et Al.)
Streak to Spot Ratio
Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping
Physical Review Materials
Additional Resources
Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images (Horwath, Et Al.)
Reflection High-Energy Electron Diffraction (Shuji Hasegawa)
Previous
Detecting kinetic transition
Last modified
2mo ago