Open Data Initiative

The DSI’s Open Data Initiative (ODI) enables us to share LLNL’s rich, challenging, and unique datasets with the larger data science community. Our goal is for these datasets to help support curriculum development, raise awareness around LLNL’s data science efforts, foster new collaborations, and be leveraged across other learning opportunities.

As we develop this catalog over time, the data will represent a wide variety of key LLNL mission areas and may include subsets of some of the world’s largest datasets. We plan to provide data ranging in complexity from dense, featureful, labeled datasets with well understood solutions to those that are sparse, noisy, and largely unexplored. These datasets can also be used to test novel hardware solutions for scalable machine learning platforms.

 

Labeled dataset | Raw dataset (2263MB each)

Sample frames reveal that the appearance of the various photo-polymerization states depends on structure design and print materials

Two-photon lithography (TPL) is a widely used 3D nanoprinting technique that uses laser light to create objects. Challenges to large-scale adoption of this additive manufacturing method include identifying light dosage parameters and monitoring during fabrication. A research team from LLNL, Iowa State University, and Georgia Tech is applying machine learning models to tackle these challenges—i.e., accelerate the process of identifying optimal light dosage parameters and automate the detection of part quality. Funded by LLNL’s Laboratory Directed Research and Development Program, the project team has curated a video dataset of TPL processes for parameters such as light dosages, photo-curable resins, and structures. Both raw and labeled versions of the datasets are available via the links above.

Learn more:

  • “Machine learning model may perfect 3D nanoprinting,” LLNL News, July 29, 2020.
  • X.Y. Lee, S.K. Saha, S. Sarkar, B. Giera. “Automated detection of part quality during two-photon lithography via deep learning,” Additive Manufacturing 36, December 2020. doi.org/10.1016/j.addma.2020.101444
  • X.Y. Lee, S.K. Saha, S. Sarkar, B. Giera. “Two Photon lithography additive manufacturing: Video dataset of parameter sweep of light dosages, photo-curable resins, and structures,” Data in Brief 32, October 2020. doi.org/10.1016/j.dib.2020.106119