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gary23ai/STEM2Crystal-Bench

STEM2Crystal-Bench STEM2Crystal-Bench is the benchmark for the paper "From Noisy STEM to Crystal Structure: Evidence-Structure CoDiffusion under Composition Constraints" (Chen & You, KDD 2026, Oral), which introduces STEM2Crystal CoDiffusion (SCCD). It evaluates methods that reconstruct a crystal structure from a noisy STEM image when the composition is known. The release has a large synthetic set with controlled noise and a small set of real STEM images, with ground-truth CIFs… See the full description on the dataset page: https://huggingface.co/datasets/gary23ai/STEM2Crystal-Bench.

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STEM2Crystal-Bench

STEM2Crystal-Bench is the benchmark for the paper "From Noisy STEM to Crystal Structure: Evidence-Structure CoDiffusion under Composition Constraints" (Chen & You, KDD 2026, Oral), which introduces STEM2Crystal CoDiffusion (SCCD). It evaluates methods that reconstruct a crystal structure from a noisy STEM image when the composition is known. The release has a large synthetic set with controlled noise and a small set of real STEM images, with ground-truth CIFs and a fixed test split.

Code and a live leaderboard: https://github.com/PEESEgroup/STEM2Crystal-Bench

Contents

The release has two test configs:

Config#samplesSourceRole
synthetic1021Physics-inspired forward pipeline (paper Appendix A.3) applied to curated 2D slab prototypes from C2DB + MC2DPrimary evaluation used in paper Table 1 under three noise regimes (low / mid / high)
real_stem_eval55Real, open-licensed atomic-resolution STEM images of 2D monolayer materials, paired with single-layer (monolayer) ground-truth CIFsOut-of-distribution real-microscopy probe (paper §5.4, Table 2)

Folder structure

STEM2Crystal-Bench/
├── README.md
├── LICENSE                        # CC BY 4.0
├── synthetic/
│   ├── cifs/                      # 1021 ground-truth CIFs (C2DB + MC2D)
│   ├── images/
│   │   ├── low/                   # 3063 images (1021×3 views), low shot noise
│   │   ├── mid/                   # 3063 images, medium noise
│   │   └── high/                  # 3063 images, high noise
│   ├── masks/                     # atom-position target masks
│   ├── split.json                 # test split IDs (same as paper §5)
│   └── metadata.jsonl             # per-sample lattice/formula/SG
└── real_stem_eval5/
    ├── cifs/                      # 5 COD ground-truth CIFs
    ├── images/                    # 5 real experimental STEM PNGs
    └── metadata.jsonl             # per-sample with source attribution

All images in synthetic/ are resized to 256×256 (the input size used by SCCD and baselines). The full-resolution abTEM output (1286×866 per image, ≈88 GB total) is not redistributed here; the simulation recipe is released with the paper's code.

Loading

python
from datasets import load_dataset

synth = load_dataset("gary23ai/STEM2Crystal-Bench", "synthetic", split="test")
real  = load_dataset("gary23ai/STEM2Crystal-Bench", "real_stem_eval5", split="test")

Each sample exposes mp_id, formula, lattice parameters (a, b, c, alpha, beta, gamma), space_group, and per-noise image paths.

real_stem_eval5/ config — data sources

The 5 real experimental samples are all genuine 2D monolayer materials (4 transition-metal dichalcogenides spanning Mo/W × S/Se/Te, plus graphene), assembled as an out-of-distribution real-microscopy probe. Each image is redistributed here only because the original source is open-licensed (CC BY 4.0 or CC BY 3.0); the ground truth is the single-layer (monolayer) structure, so a 2D-slab model can match it without prototype templating. We explicitly acknowledge the original authors below.

#sidMaterialPhaseImage sourceSource paperDOILicense
1real01_MoTe2_monolayermonolayer 2H-MoTe₂hexagonal P-6m2 (monolayer)Zenodo 5520169Guo et al., npj Comput Mater (2021)10.1038/s41524-021-00642-1CC BY 4.0
2real02_WSe2_monolayermonolayer 2H-WSe₂hexagonal P-6m2 (monolayer)Zenodo 7696721Tan et al., npj Comput Mater (2023)10.1038/s41524-023-01042-3CC BY 4.0
3real03_WS2_monolayermonolayer 2H-WS₂ (+S vacancies)hexagonal P-6m2 (monolayer)figshare 25310755Wang et al., Nat Commun (2024)10.1038/s41467-024-53880-4CC BY 4.0
4real04_MoS2_monolayermonolayer 1H-MoS₂hexagonal P-6m2Wikimedia Commons MoS2_vacancies.jpgHong et al., Nat Commun 6 (2015)10.1038/ncomms7293CC BY 4.0
5real05_graphenemonolayer graphenehoneycomb P6/mmmWikimedia Commons Graphene-TEM.jpgGass et al., Nat Nanotechnol 3 (2008)10.1038/nnano.2008.280CC BY 3.0

Ground-truth CIFs are the single-layer (monolayer) structures (TMD layers derived from their COD bulk entries; MoS₂ and graphene use the canonical monolayer cells). Source paper PDFs are not redistributed; please consult the DOIs above.

Citation

If you use STEM2Crystal-Bench, please cite our paper (KDD 2026, Oral) and the original source papers for the real_stem_eval5 images listed above.

bibtex
@inproceedings{chen2026stem2crystal,
  title     = {From Noisy STEM to Crystal Structure: Evidence-Structure CoDiffusion under Composition Constraints},
  author    = {Chen, Guangyao and You, Fengqi},
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2026},
  note      = {Oral presentation}
}

Code + live leaderboard: https://github.com/PEESEgroup/STEM2Crystal-Bench

License

  • —Benchmark assembly, metadata, splits, synthetic STEM renderings: CC BY 4.0
  • —Ground-truth CIFs (synthetic/cifs/: 764 from C2DB + 257 from MC2D; real_stem_eval5/cifs/: monolayer structures): upstream licenses (C2DB is CC BY 4.0, MC2D is CC BY 4.0 via Materials Cloud, COD is public-domain)
  • —Real STEM images (real_stem_eval5/images/): CC BY 4.0 (four TMD monolayers) and CC BY 3.0 (graphene), redistributed under the terms of the original open-license sources (Zenodo / figshare / Wikimedia Commons), with full attribution to the source authors in the table above
gary23ai/STEM2Crystal-Bench · CoolFace