> For the complete documentation index, see [llms.txt](https://atomic-data-sciences.gitbook.io/home/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://atomic-data-sciences.gitbook.io/home/literature/rheed-analysis.md). # 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.)*](https://www.jmlr.org/papers/volume22/20-1061/20-1061.pdf) #### HDBSCAN [*hdbscan: Hierarchical density based clustering (McInnes, Et AL.)*](https://www.theoj.org/joss-papers/joss.00205/10.21105.joss.00205.pdf) #### Clustering [*Machine learning analysis of perovskite oxides grown by molecular beam epitaxy (*Sydney, Et Al.)](https://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.6.063805) [*Engineering ordered arrangements of oxygen vacancies at the surface of superconducting La2CuO4 thin films (Suyolcu, Et Al.)*](https://pubs.aip.org/avs/jva/article/40/1/013214/2846493/Engineering-ordered-arrangements-of-oxygen) [*Skill-Agnostic analysis of reflection high-energy electron diffraction patterns for Si(111) surface superstructures using machine learning (Asako Yoshinari, Et Al.)*](https://www.tandfonline.com/doi/full/10.1080/27660400.2022.2079942) #### Streak-to-Spot Ratio {% embed url="" %} ### Additional Resources [Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images (Horwath, Et Al.)](https://www.nature.com/articles/s41524-020-00363-x) [Reflection High-Energy Electron Diffraction (Shuji Hasegawa)](http://www-surface.phys.s.u-tokyo.ac.jp/papers/2012/CharacteriMat\(Hasegawa\)201202.pdf)