AutoEMXSp

Automated Electron Microscopy X-Ray Spectroscopy for Compositional Characterization of Materials

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AutoEMXSp is a fully automated framework for SEM-EDS workflows — from spectral acquisition and quantification, to principled filtering and compositional analysis — all in one click.

🎥 Watch AutoEMXSp in action on a desktop SEM-EDS system at https://youtu.be/Bym58gNxlj0

🧪 Test AutoEMXSp to fit and quantify EDS spectra you have collected on your commercial SEM-EDS system (see Tutorials).

📖 This work is described in:
A. Giunto et al., Accurate SEM‑EDS Quantification, Automation, and Machine Learning Enable High‑Throughput Compositional Characterization of Powders, 2025.
DOI: https://doi.org/10.21203/rs.3.rs-7837297/v2

✨ Key Features

Fully automated SEM-EDS phase-level compositional analysis workflow, which includes:
- Acquisition of EDS spectra, including particle localization if sample is powder. Compatible also with bulk samples, or manual navigation.
- Quantification of compositions using the peak-to-background method
- Rule-based filtering of compositions to discard poorly quantified spectra from the analysis
- Unsupervised machine learning–based analysis to identify the compositions of individual phases in the sample
Scripts for fitting and quantification of single EDS spectra exported by proprietary commercial software (.msa, .emsa, .txt files)
Automated experimental standard collection scripts
Automated particle size distribution measurements scripts
Extensible architecture — adaptable to other techniques such as
- Wavelength Dispersive Spectroscopy (WDS)
- Scanning Transmission Electron Microscopy (STEM) with EDS
Extensible hardware support — includes driver for ThermoFisher Phenom Desktop SEM series, and can be extended to any electron microscope with a Python API

📊 Performance

Benchmarked on 74 single-phase samples with compositions spanning 38 elements (from nitrogen to bismuth), it achieved <5–10% relative deviation from expected values
Machine learning compositional analysis detects individual phase composition in multi-phase samples, including minor phases
Intermixed phases can also be resolved

🧪 Supported Use Cases

Scanning Electron Microscopy (SEM) with Energy-Dispersive Spectroscopy (EDS)
Powders and rough samples, e.g. rough films, or pellets, with automated segmentation.
Bulk, flat samples, navigated by defining a grid of points to probe.
Manual navigation of any sample.

⚙️ Requirements

Cross-platform: runs on Linux, macOS, and Windows
Quick installation
Requires calibration for use with different electron microscopes for maximum compositional accuracy, though EDS quantification can be tested without recalibration.

📘 Documentation

Installation instructions, usage examples, and workflow descriptions are available in the AutoEMXSp documentation:

👉 https://cedergrouphub.github.io/AutoEMXSp/

📦 Requirements

Python 3.11 or newer
All dependencies are installed automatically via pip or conda.
Tested versions of dependencies are specified in pyproject.toml.

The package may work with more recent versions, but these have not been tested.

Electron Microscope Support

✅ Developed and tested for Thermo Fisher Phenom Desktop SEMs.
✅ Compatible with any Phenom microscope equipped with PPI (Phenom Programming Interface).
⚠️ For other microscope models, the driver must be adapted to the appropriate API commands.

🆕 Coming Soon

Here’s what’s planned for future releases of AutoEMXSp:

🐍 Verify with the latest Python version for improved compatibility with current scientific libraries
📏 New scripts for spectral parameter calibration to extend the XSp_calibs library to your own instrument.

📂 Project Structure

The repository is organized as follows:

AutoEMXSp/
├── autoemxsp/                 # Main package source code
│   ├── _custom_plotting.py     # Customizable clustering plot function
│   ├── config/                 # Configuration files, including default values to employ during measurements.
│   ├── core/                   # Core objects and source code
│   ├── data/                   # Libraries of X-ray data
│   ├── EM_driver/              # Electron Microscope driver (⚠️ adapt to your own instrument)
│   └── Results/                # Example acquired data (used for unit tests)
│   ├── runners/                # Runner functions calling on core objects
│   ├── scripts/                # Scripts to run acquisition, quantification, etc. (see full list below)
│   ├── XSp_calibs/             # X-ray spectral calibrations (⚠️ adapt to your own instrument)
│   ├── utils/                  # Utility functions and strings employed by the program
│
├── examples/                  # Example scripts for fitting, quantification and compositional analysis of example data
├── tests/                     # Unit tests for fitting, quantification, compositional analysis and image processing
│                               # (Acquisition tests require proper EM drivers)
├── paper_data/                # Raw paper data uploaded on Git LFS (Dowload instructions in Paper Data section below)
│
├── LICENSE.txt
├── README.md
└── pyproject.toml

📁 Scripts

This repository includes a collection of scripts that streamline the use of AutoEMXSp.
Each script is tailored for a specific task in spectral acquisition, calibration, quantification, or analysis.

🔬 Acquisition, Quantification & Analysis

Run_Acquisition_Quant_Analysis.py — Acquire X-ray spectra and optionally perform quantification and composition analysis.
Run_Quantification_Analysis.py — Quantify acquired spectra (single or multiple samples) and perform machine-learning analysis.
Run_Analysis.py — Launch customized machine-learning analysis on previously quantified data.
Fit_Quant_Single_AutoEMXSp_Spectrum.py — Fit and optionally quantify a single spectrum measured with AutoEMXSp. Prints fitting parameters and plots fitted spectrum for detailed inspection of model performance.
Fit_Quant_Single_MSA_Spectrum.py — Fit and optionally quantify a single spectrum exported by proprietary software.

📊 Particle Size Distribution Measurements

Collect_Particle_Statistics.py - Analyse sample, collecting particle size statistics and distribution.
Process_Particle_Stats_Files.py - Process acquired aprticle size data and recompute.

🛠️ Miscellaneous

Run_Experimental_Standard_Collection.py — Acquire and fit experimental standards.
Run_SDD_Calibration.py — Perform calibration of the SDD detector.

⚗️ Characterize Extent of Intermixing in Known Powder Mixtures

(see Chem. Mater. 2025, 37, 6807−6822 for example)
Use the same scripts as regular composition characterization, as described in the docs Tutorial.

👉 All scripts can be executed directly from the command line or imported into a Python environment, making them accessible from anywhere on your system.

🤝 Contributing

Contributions are welcome!

Open to collaborations to extend this package to different tools or to different types of samples, for example thin films. Please contact me at agiunto@lbl.gov

📄 License

This project is licensed under a NON-COMMERCIAL USE ONLY, LICENSE — see the LICENSE file for details.

📖 Citation

If you use AutoEMXSp in your research, please cite the following publication:

A. Giunto, Y. Fei, P. Nevatia, B. Rendy, N. Szymanski and G. Ceder; Accurate SEM‑EDS Quantification, Automation, and Machine Learning Enable High‑Throughput Compositional Characterization of Powders, 2025.
DOI: https://doi.org/10.21203/rs.3.rs-7837297/v1

BibTeX

@article{Giunto2025AutoEMXSp,
  author  = {Giunto, Andrea and Fei, Yuxing and Nevatia, Pragnay and Rendy, Bernardus and Szymanski, Nathan and Ceder, Gerbrand},
  title   = {Accurate SEM‑EDS Quantification, Automation, and Machine Learning Enable High‑Throughput Compositional Characterization of Powders},
  year    = {2025},
  doi     = {10.21203/rs.3.rs-7837297/v2},
  url     = {https://doi.org/10.21203/rs.3.rs-7837297/v2}
}

📂 Paper Data

The raw data used in the associated publication is stored in the paper_data/ directory.
These files are tracked with Git LFS (Large File Storage).

🔽 Download with Git LFS

The repository is automatica

AutoEMXSp

Install / Use

README

AutoEMXSp

✨ Key Features

📊 Performance

🧪 Supported Use Cases

⚙️ Requirements

📑 Table of Contents

📘 Documentation

📦 Requirements

Electron Microscope Support

🆕 Coming Soon

📂 Project Structure

📁 Scripts

🔬 Acquisition, Quantification & Analysis

📊 Particle Size Distribution Measurements

🛠️ Miscellaneous

⚗️ Characterize Extent of Intermixing in Known Powder Mixtures

🤝 Contributing

📄 License

📖 Citation

BibTeX

📂 Paper Data

🔽 Download with Git LFS