metadata
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 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
# 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 license.
Citation
@inproceedings{stemgym2026,
title={STEMGym: A Benchmark for Dose-Efficient Autonomous STEM Microscopy},
author={Polat, John},
year={2026}
}