HEDM (BraggNN)

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Date: 2023-10-03

Name: HEDM BraggNN

Domain: Materials Science

Focus: Fast Bragg peak analysis using deep learning in diffraction microscopy

Task Types: Peak detection

Metrics: Localization accuracy, Inference time

Models: BraggNN

AI/ML Motif: Classification

Resources

Benchmark: Visit

Results: Gemini LLM Deep Research

Keywords

BraggNN diffraction peak finding HEDM

Citation

• Zhengchun Liu, Hemant Sharma, Jun-Sang Park, Peter Kenesei, Antonino Miceli, Jonathan Almer, Rajkumar Kettimuthu, and Ian Foster. Braggnn: fast x-ray bragg peak analysis using deep learning. 2021. URL: https://arxiv.org/abs/2008.08198, arXiv:2008.08198.

@misc{liu2021braggnnfastxraybragg,
  archiveprefix = {arXiv},
  author        = {Zhengchun Liu and Hemant Sharma and Jun-Sang Park and Peter Kenesei and Antonino Miceli and Jonathan Almer and Rajkumar Kettimuthu and Ian Foster},
  eprint        = {2008.08198},
  primaryclass  = {eess.IV},
  title         = {BraggNN: Fast X-ray Bragg Peak Analysis Using Deep Learning},
  url           = {https://arxiv.org/abs/2008.08198},
  year          = {2021}
}

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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