espm: A Python library for the simulation of STEM-EDXS datasets

Area of Science:

• Materials Science
• Computational Science
• Chemistry

Background:

• Scanning Transmission Electron Microscopy Energy-Dispersive X-ray Spectroscopy (STEM-EDX) generates complex datacubes requiring advanced analysis techniques.
• Developing accurate simulations is crucial for validating analytical methods and understanding experimental limitations.

Purpose of the Study:

• To introduce two open-source Python packages, espm and emtables, for simulating STEM-EDX datacubes.
• To provide a framework for testing decomposition algorithms on STEM-EDX data with a known ground truth.
• To assist in experimental design for electron microscopy.

Main Methods:

• The espm package simulates STEM-EDX datacubes using user-defined chemical compositions and spatial abundance maps.
• X-ray emission cross-sections are calculated using the emtables package, which is based on state-of-the-art computations.
• The simulation framework was validated by comparing simulated and experimental datasets of a complex geological sample using non-negative matrix factorization.

Main Results:

• The developed Python packages enable the simulation of realistic STEM-EDX datacubes.
• The simulation framework allows for the rigorous testing and validation of data analysis algorithms.
• The study demonstrated the utility of the packages in analyzing a complex geological sample.

Conclusions:

• The espm and emtables packages offer valuable tools for researchers in electron microscopy and materials science.
• These open-source packages can accelerate the development and application of advanced analytical techniques for STEM-EDX data.
• The software facilitates improved experimental design and data interpretation in nanoscale chemical analysis.