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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)​
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Detecting kinetic transition

Last modified 2mo ago