STEMGym: A Gymnasium Environment for Benchmarking Dose-Efficient Autonomous Scanning Transmission Electron 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-HAADF images (128x128 px tiles, 4px overlap, stride=124) arranged in an 8x8 grid
• Ground truth annotations: Atom positions, defect types, and phase maps for scoring
• Metadata: Pixel size, accelerating voltage, detector geometry, material parameters

Materials

Material	Zone Axis	FOV	Defect Types	Task
SrTiO3	[001]	~100 nm	O vacancies clustered along grain boundary	Defect Census
BaTiO3	[001]	~100 nm	Cubic/tetragonal phase boundaries + O vacancies	Phase Mapping + Defect Census
SiGe	[110]	~50 nm	Ge substitutions concentrated in one quadrant	Targeted Characterization
GaN	[11-20]	~80 nm	InGaN quantum-well substitutions	Defect Census
Pt nanoparticles	—	~60 nm	Pt nanoparticles on amorphous carbon	Particle Census

Difficulty Levels

Each material is provided at three difficulty levels:

Level	Vacancy Rate	Phonon Configs	Dose (e-/A^2)
Easy	5%	4	1e4
Medium	3%	8	5e3
Hard	1%	16	1e3

Files

Simulated Worlds (worlds/)

File	Material	Difficulty	Size
test_world.h5	Synthetic (Gaussian blobs)	—	9.7 MB
replay_world.h5	Synthetic (replay validation)	—	2.5 MB
srtio3_clustered_easy.h5	SrTiO3	Easy	884 MB
srtio3_clustered_medium.h5	SrTiO3	Medium	884 MB
srtio3_clustered_hard.h5	SrTiO3	Hard	885 MB
batio3_interface_easy.h5	BaTiO3	Easy	911 MB
batio3_interface_medium.h5	BaTiO3	Medium	911 MB
batio3_interface_hard.h5	BaTiO3	Hard	911 MB
sige_gradient_clustered_easy.h5	SiGe	Easy	150 MB
sige_gradient_clustered_medium.h5	SiGe	Medium	148 MB
sige_gradient_clustered_hard.h5	SiGe	Hard	147 MB
gan_easy.h5	GaN	Easy	451 MB
gan_medium.h5	GaN	Medium	449 MB
gan_hard.h5	GaN	Hard	448 MB
pt_nanoparticles_easy.h5	Pt	Easy	129 MB
pt_nanoparticles_medium.h5	Pt	Medium	129 MB
pt_nanoparticles_hard.h5	Pt	Hard	129 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
dqn_agent.zip	DQN (SB3)	RL navigation baseline	0.8 MB
ppo_agent.zip	PPO (SB3)	RL navigation baseline	1.0 MB
sac_agent.zip	SAC (SB3)	RL navigation baseline	8.4 MB

Transfer Checkpoints (checkpoints/transfer/)

Material-specific analyst models trained on individual materials for transfer experiments:

Directory	Contents
transfer/srtio3/	atom_finder.pt, defect_classifier.pt, phase_identifier.pt, training_metadata.json
transfer/batio3/	atom_finder.pt, defect_classifier.pt, phase_identifier.pt, training_metadata.json
transfer/sige/	atom_finder.pt, defect_classifier.pt, phase_identifier.pt, training_metadata.json
transfer/gan/	atom_finder.pt, defect_classifier.pt, phase_identifier.pt, training_metadata.json

HDF5 World Format

Each world file follows this layout:

/metadata/
    pixel_size_nm          # Physical pixel size
    tile_size_px           # Tile dimensions (128)
    grid_shape             # Grid dimensions (rows, cols)
    fov_nm                 # Field of view in nm
    scenario               # Material/scenario name
    difficulty             # easy / medium / hard
/overview                  # (H, W) low-res overview image
/tiles/{row}_{col}         # (128, 128) float32 normalized HAADF tiles
/ground_truth/
    atom_positions         # (N, 2) in nm
    atom_types             # (N,) int [0=pristine, 1=vacancy, 2=substitution]
    defect_mask            # (N,) bool
    defect_types           # (N,) str ["pristine"/"vacancy"/"substitution"]
    phase_map              # (H, W) int32, optional
/valid_region              # (H, W) bool

Usage

# Install STEMGym
pip install -e .

# Download this data
python stem_gym/scripts/download_data.py

# Run a benchmark
stemgym run --agent raster_equipped --task defect_census --world srtio3_clustered_easy --seeds 3

License

This dataset is released under the CC-BY-4.0 license.

Citation

@inproceedings{polat2026stemgym,
  title={STEMGym: A Gymnasium Environment for Benchmarking Dose-Efficient Autonomous Scanning Transmission Electron Microscopy},
  author={Polat, Can},
  year={2026}
}