A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy
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A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy

  • Author(s): Zhang, Yan
  • Farrell, Steve
  • Crowley, Michael
  • Makowski, Lee
  • Deslippe, Jack
  • et al.
Abstract

An image dataset of 10 different size molecules, where each molecule has 2,000 structural variants, is generated from the 2D cross-sectional projection of Molecular Dynamics trajectories. The purpose of this dataset is to provide a benchmark dataset for the increasing need of machine learning, deep learning and image processing on the study of scattering, imaging and microscopy.

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