Dataset: Automated iterative refinement of uncertain parameters in an optical floating zone experiment and temperatures obtained using optimized parameters
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Published: 4 years ago Views: 649 Downloads: 221 DOI: 10.13011/m3-g1ek-5g42 License: Attribution License (ODC-By) Size: 2.78 MB
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  • Guanglong Huang
  • Mojue Zhang
  • David Montiel
  • Praveen Soundararajan
  • Yusu Wang
  • Jonathan Denney
  • Adam Corrao
  • Peter Khalifah
  • Katsuyo Thornton

This dataset contains the raw and processed data used in the manuscript in revision titled "Automated extraction of physical parameters from experimentally obtained thermal profiles using a machine learning approach". This dataset contains (1) sampled vectors and their errors at each iteration, (2) the experimental and simulated temperature profiles (using the optimized parameters) in optical floating zone experiments, and (3) the experimental and simulated time dependent temperatures (using the optimized parameters) in optical floating zone experiments. The dataset is subject to be updated in the revision process.

This work was supported as part of GENESIS: A Next Generation Synthesis Center, an Energy Frontier Research Center funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences under award No. DE-SC0019212. This research used beamline 28-ID-1 of the National Synchrotron Light Source II, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No. DE-SC0012704.

  • Automated extraction of physical parameters from experimentally obtained thermal profiles using a machine learning approach
    Huang, G., Zhang, M., Montiel, D., Soundararajan, P., Wang, Y., Denney, J. J., Corrao, A. A., Khalifah, P. G. and Thornton, K. Automated Extraction of Physical Parameters from Experimentally Obtained Thermal Profiles Using a Machine Learning Approach. Computational Materials Science 194, 110459, doi:10.1016/j.commatsci.2021.110459 (2021).
    https://www.sciencedirect.com/science/article/abs/pii/S0927025621001841
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