DINO-4DSTEM

A self-supervised deep-learning pipeline for 4D-STEM data analysis.

Unsupervised classification and phase mapping for 4D-STEM (4DSTEM, four-dimensional STEM) and scanning electron diffraction (SED) — also nanobeam electron diffraction (NBED) and nanodiffraction. It groups your diffraction patterns into distinct structural regions and shows you why — no labels, no coding.

⬇ Download & install →

Free · Windows · a one-time ~15-minute setup, then double-click to run.

What it does

Point it at a scan. It groups every diffraction pattern into structurally distinct classes — phases, crystallinity, texture — paints them on a map of your sample, and gives you the evidence behind each class.

See the evidence — at any scale

Every class is checkable. Look at its diffraction as a single frame, a grain average, or the whole-class average — the noise falls away and the Bragg spots sharpen as you zoom out.

Bring your own data

Point it at the files you already have — nothing is uploaded, everything stays on your machine.

Low dose? Denoise it first.

Beam-sensitive samples force you below the dose that gives a clean pattern. The Pre-processing tab now has one-click self-supervised denoising — no clean reference, no repeated acquisition. One exposure is enough.

🌀 Denoise (Noise2Noise, binomial). Every pattern is a Poisson draw, so a fair coin flip per electron deals its counts into two independent half-dose exposures of the same signal (Poisson thinning). A small, deliberately low-capacity dose-equivariant network learns to map one half to the other, then runs on the full exposure. Total counts are preserved — and there is no parameter to tune.

📉 Flatten (radial background). Removes the azimuthally-symmetric radial background — the direct-beam skirt and the amorphous halo — so the Bragg and orientation signal stands out. Subtract the azimuthal median, or divide by the azimuthal mean.

Both show a before/after preview, then bake a new cube alongside the original — your raw file is never modified.

The other algorithms are in the notebooks. n2_methods_demo.ipynb compares Noise2Void, Noise2Self and scan-to-scan Noise2Noise against the binomial split — with the math behind each, and why the blind-spot methods carry a hyperparameter while the binomial split does not.

More than classes — the full 4D-STEM toolbox

The learned class map is the centre of the app, but the everyday 4D-STEM workflow is all there too — on the same loaded scan, no scripting.

Orientation and phase mapping is py4DSTEM's. The ACOM tab's CIF mode is a graphical front end to py4DSTEM's crystal orientation and phase mapping (Crystal.orientation_plan / match_orientations, CrystalPhase.quantify_phase) — the matching and the NNLS phase fit are py4DSTEM's work, not ours (the Data reference and Strain modes are DINO-4DSTEM's own spot matching and lattice fitting; a CIF reference there uses py4DSTEM only to simulate the spot positions). If you use it, please cite py4DSTEM (Savitzky et al., Microscopy and Microanalysis 2021) and its orientation-mapping paper (Ophus et al., Microscopy and Microanalysis 2022).

Where results go. Every analysis tab saves into one folder next to your data — <data name>_DINO4DSTEM/ with a sub-folder per tool (nmf/, acom/maps, acom/dataref, acom/strain, …) and a README inside. The Open outputs folder button in the top bar takes you there.

Get started — pick your platform

Each bundle is the whole app with the right launchers for your OS. All three need free Miniconda / Miniforge installed first.

Prefer git? git clone https://github.com/DanielKhaykelson/dino-4dstem, then run the installer for your OS. The Windows bundle also includes GETTING_STARTED.docx, a picture-by-picture walkthrough.

Or install with pip

Comfortable with Python? Install into a fresh Python 3.10 environment:

pip install dino4dstem
dino4dstem shortcuts     # optional: GUI / Assistant / Notebooks icons on your Desktop
dino4dstem setup-gpu     # optional: CUDA build of PyTorch for an NVIDIA GPU
pip install -U dino4dstem   # later: update to the newest release

Then just run dino4dstem to open the app. Other commands: dino4dstem assistant, dino4dstem notebooks, dino4dstem --help. The app keeps its working files in ~/DINO4DSTEM (set DINO4DSTEM_HOME to move it).

⚠️ Windows is the primary, fully tested platform. The Linux and macOS builds share the same code and their installers have been checked, but they have not yet been fully tested end-to-end on those systems. If you hit a problem on Linux or macOS, please get in touch — reports are very welcome.

Citing

If DINO-4DSTEM contributes to work you publish, please cite the paper:

DINO4DSTEM: A self-supervised framework for structural discovery in 4D-STEM
Daniel Khaykelson, Lothar Houben, Boris Rybtchinski
arXiv:2608.15098 (2026) — read it →

@article{khaykelson2026dino4dstem,
  title  = {DINO4DSTEM: A self-supervised framework for structural discovery in 4D-STEM},
  author = {Khaykelson, Daniel and Houben, Lothar and Rybtchinski, Boris},
  journal = {arXiv preprint arXiv:2608.15098},
  year   = {2026},
  url    = {https://arxiv.org/abs/2608.15098}
}

GitHub also builds this for you: use “Cite this repository” on the repo sidebar (it reads CITATION.cff) to copy BibTeX or APA.

Learn more

• 📄 The paper — arXiv:2608.15098
• 🧭 What it does & results — PROJECT_SUMMARY.md
• 📓 Tutorial notebooks — notebooks/ — phantom → train → analyse → baselines, plus the denoising methods comparison (N2N binomial, Noise2Void, Noise2Self)
• 🧠 The methods explained (DINO, NMF, clustering, PCA) — docs/assistant/CONCEPTS.md
• 📕 Full manual — docs/USER_MANUAL.pdf
• 🛠️ Install guide — INSTALL.md

Help & contact

Questions, bug reports, feature requests, or help getting it running on your data — including Linux/macOS issues — are all welcome. Two ways to reach us:

• 🐛 Bug reports & feature requests: open a GitHub issue (click New issue). Best for anything reproducible — it's searchable and others can follow along.
• 📧 Direct questions / inquiries: email Daniel Khaykelson at daniel.kh@weizmann.ac.il.

When reporting a problem, it helps to include your operating system, what you did, and the full error message (copy the text from the console window).