# EMSeek Dataset Overview

## Background
The EMSeek dataset accompanies the paper *Bridging Electron Microscopy and Materials Analysis with an Autonomous Agentic Platform* (Chen et al., 2025). It supports the system's reference-guided, single-click segmentation workflows and provides curated examples for downstream lattice reconstruction, property prediction, and literature-grounded reasoning. The collection spans atomic-resolution and nanoscale transmission electron microscopy scenarios so that SegMentor, EM2CIF, MatProphet, and related agents can be trained and evaluated on a unified benchmark of heterogeneous materials tasks.

## Directory Layout
Task-specific data are organized at the repository root:

```
atom_columns/
atomic_defects/
metal_alloy_defects/
nanoparticles/
single_atom_catalysts/
README.md
```

Each task directory contains one folder per material or specimen. Inside every material folder the files follow a consistent structure:

```
<task>/<material>/
├── image/        # Raw microscopy frames (.png)
├── label/        # Pixel-wise masks (.png)
├── bbox/         # YOLO-style annotations (.txt)
└── prompt.txt    # Optional textual prompt (one-word / concise / detailed)
```

## Task Coverage
### Atomic Column Segmentation (`atom_columns`)
Contains annular dark-field STEM frames of crystalline materials such as perovskites, transition-metal dichalcogenides, metals, silicon, and graphene. Images and masks are stored at 256×256 resolution to emphasize lattice periodicity, while accompanying bounding boxes mark atomic-column centroids used by the SegMentor agent.

### Atomic Defect Identification (`atomic_defects/WSe2`)
Provides high-resolution WSe₂ micrographs featuring irradiation-induced point defects. RGB masks distinguish background lattice regions from localized defects, and bounding boxes highlight each defect center to support both semantic segmentation and detection workflows.

### Metal Alloy Defects (`metal_alloy_defects/FeCrAl`)
Captures FeCrAl alloy foils imaged under differing magnifications. Binary masks delineate irradiation damage, and prompts summarize defect characteristics for conditioning language-aware agents. Image resolutions vary between 1,024×1,024 and 2,048×2,048 pixels to preserve mesoscale context.

### Nanoparticle Analysis (`nanoparticles`)
Includes supported nanoparticle systems (for example Au@C, Au@Ge, Au@SiN, CdSe@C, Fe₃O₄, Fe₃O₄@SiO₂, Pd@C). Masks trace particle outlines for downstream size statistics and morphology analysis, while bounding boxes provide coarse localization for detection models. Large-format micrographs retain their native dimensions so users can perform multi-scale cropping when training.

### Single-Atom Catalysts (`single_atom_catalysts`)
Reserved for future releases. The directory remains as a placeholder for upcoming single-atom catalyst annotations referenced in the paper.

## Annotation Formats
- **Images (`image/*.png`)**: Grayscale microscopy frames. Atomic column data are normalized to 256×256, whereas other tasks keep their original resolution.
- **Masks (`label/*.png`)**: Typically encoded as binary grayscale (0 = background, 255 = target). The WSe₂ defect set uses an RGB palette to represent multiple semantic categories.
- **Bounding boxes (`bbox/*.txt`)**: Provided in normalized YOLO format as `<class> <x_center> <y_center> <width> <height>`. Coordinates and extents are expressed between 0 and 1.
- **Prompts (`prompt.txt`)**: Contain short, medium, and detailed textual descriptions that can be fed to language-conditioned agents to reproduce the EMSeek planning workflow.

## Usage Notes
- Use `image/` and `label/` pairs to train segmentation models; `bbox/` files can drive detection or assist with weakly supervised learning.
- For integration with the EMSeek pipeline, pass segmentation masks to EM2CIF for mask-aware lattice reconstruction and then to MatProphet for property inference, recreating the paper's pixels-to-properties loop.
- When handling large nanoparticle images, consider tile-based sampling, multi-scale augmentations, or down-sampling to fit GPU memory constraints. RGB masks in the defect task can be converted to index maps if a network expects single-channel labels.
- Some directories may contain system-generated files (for example `._*.png`); filter them out during preprocessing to avoid loading errors.

## License and Citation
- License: Apache License 2.0.
- When using this dataset, please cite the original work:

> Chen, G., Yuan, W., & You, F. (2025). *Bridging Electron Microscopy and Materials Analysis with an Autonomous Agentic Platform*. Cornell University AI for Science Institute.

For the latest updates, visit the project repository: <https://github.com/iCGY96/EMSeek>.