4D-STEM

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Date: 2023-12-03

Name: 4D-STEM

Domain: Materials Science

Focus: Real-time ML for scanning transmission electron microscopy

Task Types: Image Classification, Streamed data inference

Metrics: Classification accuracy, Throughput

Models: CNN models (prototype)

AI/ML Motif: Classification

Resources

Benchmark: Visit

Results: Gemini LLM Deep Research

Keywords

4D-STEM electron microscopy real-time image processing

Citation

• Shuyu Qin, Joshua Agar, and Nhan Tran. Extremely noisy 4d-tem strain mapping using cycle consistent spatial transforming autoencoders. In AI for Accelerated Materials Design - NeurIPS 2023 Workshop. 2023. URL: https://openreview.net/forum?id=7yt3N0o0W9.

@inproceedings{qin2023extremely,
  title={Extremely Noisy 4D-TEM Strain Mapping Using Cycle Consistent Spatial Transforming Autoencoders},
  author={Shuyu Qin and Joshua Agar and Nhan Tran},
  booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
  year={2023},
  url={https://openreview.net/forum?id=7yt3N0o0W9}
}

Ratings

CategoryRating

Software

2.00

No standalone code repository or setup instructions provided

Specification

5.00

None

Dataset

2.00

No dataset links or FAIR metadata; unclear public access

Metrics

4.00

Only localization accuracy and inference time mentioned; not formally benchmarked with scripts

Reference Solution

3.00

BraggNN model is described and evaluated, but no direct implementation or inference scripts available

Documentation

3.00

Paper is clear, but lacks a GitHub repo or full reproducibility pipeline

Average rating: 3.17/5

Radar plot

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