Introduction

STEM-EDX ML is a graphical application designed for managing and analyzing STEM-EDX data.

It allows users to load data, apply decomposition algorithms (PCA, NMF), and visualize results.

STEM-EDX is a technique used to analyze the composition of materials at the nanoscale. It combines scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDX) to provide detailed information about the elemental composition of a sample.

Using a microscope, a focused electron beam is scanned across a sample, generating X-rays that are characteristic of the elements present. These X-rays are collected and analyzed to determine the elemental composition of the sample.

Each pixel in the resulting datacube represents the intensity of X-rays emitted by the sample at a specific energy. By analyzing this data, researchers can identify the elements present in the sample and map their distribution.

Machine learning algorithms can be applied to STEM-EDX data to extract meaningful information and identify patterns in the data. Principal component analysis (PCA) and non-negative matrix factorization (NMF) are commonly used decomposition algorithms that can help researchers identify trends and relationships in the data. These algorithms can reduce the dimensionality of the data and highlight important features that may not be immediately apparent.

STEM-EDX ML provides a user-friendly interface for loading and analyzing STEM-EDX data, making it easier for researchers to explore and interpret their results.

For more information on how to install and use STEM-EDX ML, refer to the Installation and Usage sections of this documentation.