RHEED Analysis

Supporting Literature

Unsupervised Algorithms

Atomscale 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 mappingPhysical 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)