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license: cc-by-4.0
task_categories:
- reinforcement-learning
tags:
- stem-microscopy
- electron-microscopy
- materials-science
- gymnasium
- benchmark
- dose-efficiency
pretty_name: STEMGym
size_categories:
- 1K<n<10K
---
# STEMGym: Benchmark Data for Dose-Efficient Autonomous STEM Microscopy
This dataset accompanies the [STEMGym](https://github.com/KurbanIntelligenceLab/STEMGym) benchmark — a Gymnasium-based environment for evaluating autonomous dose-efficient scanning transmission electron microscopy (STEM) agents.
## Dataset Description
STEMGym provides simulated STEM specimens as HDF5 world files. Each world contains:
- **Overview image**: Low-magnification survey of the full specimen
- **Tile grid**: High-resolution STEM images (128×128 px tiles, 4px overlap, stride=124) arranged in an 8×8 grid
- **Ground truth annotations**: Atom positions, defect types, and phase maps for scoring
- **Metadata**: Pixel size, accelerating voltage, detector geometry, material parameters
### Materials
| Material | Description | Defect Types |
|----------|-------------|-------------|
| SrTiO₃ | Perovskite oxide | O vacancies, Sr vacancies, Ti antisites |
| BaTiO₃ | Ferroelectric perovskite | O vacancies, Ba vacancies, domain boundaries |
| SiGe | Semiconductor alloy | Ge substitutions, strain fields |
### Difficulty Levels
Each material is provided at three difficulty levels (easy, medium, hard) controlling defect density, noise level, and spatial distribution.
## Files
### Simulated Worlds (`worlds/`)
| File | Material | Difficulty | Size |
|------|----------|-----------|------|
| `test_world.h5` | Synthetic (Gaussian blobs) | — | ~5 MB |
| `srtio3_easy.h5` | SrTiO₃ | Easy | ~50 MB |
| `srtio3_medium.h5` | SrTiO₃ | Medium | ~50 MB |
| `srtio3_hard.h5` | SrTiO₃ | Hard | ~50 MB |
| `batio3_easy.h5` | BaTiO₃ | Easy | ~50 MB |
| `batio3_medium.h5` | BaTiO₃ | Medium | ~50 MB |
| `batio3_hard.h5` | BaTiO₃ | Hard | ~50 MB |
| `sige_easy.h5` | SiGe | Easy | ~50 MB |
| `sige_medium.h5` | SiGe | Medium | ~50 MB |
| `sige_hard.h5` | SiGe | Hard | ~50 MB |
### Model Checkpoints (`checkpoints/`)
| File | Model | Description | Size |
|------|-------|-------------|------|
| `atom_finder.pt` | AtomFinderUNet | Atomic column detection ensemble (3 members) | ~88 MB |
| `defect_classifier.pt` | DefectClassifierCNN | Defect type classification | ~1.4 MB |
| `phase_identifier.pt` | PhaseIdentifierResNet | Material phase identification | ~7.4 MB |
## HDF5 World Format
Each world file follows this layout:
```
/metadata/
pixel_size_nm # Physical pixel size
tile_size_px # Tile dimensions (128)
tile_overlap_px # Overlap between tiles (4)
grid_rows, grid_cols # Grid dimensions
accelerating_voltage # Beam energy (keV)
convergence_angle # Probe convergence (mrad)
detector_inner/outer # HAADF detector angles (mrad)
/overview # Low-magnification survey image
/tiles/{row}_{col} # High-resolution tile images
/ground_truth/
atom_positions # (N, 3) array: x, y, atomic_number
defect_types # (M, 4) array: x, y, type_id, severity
phase_map # 2D array: phase labels per pixel
/valid_region # Boolean mask of valid scan area
```
## Usage
```bash
# Install STEMGym
pip install -e .
# Download this data
python scripts/download_data.py
# Run a benchmark
stemgym run --agent raster_equipped --task defect_census --world srtio3_medium --seeds 3
```
## License
This dataset is released under the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
## Citation
```bibtex
@inproceedings{stemgym2026,
title={STEMGym: A Benchmark for Dose-Efficient Autonomous STEM Microscopy},
author={Polat, John},
year={2026}
}
```
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