A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy
2019-11-15Unverified0· sign in to hype
Yan Zhang, Steve Farrell, Michael Crowley, Lee Makowski, Jack Deslippe
Unverified — Be the first to reproduce this paper.
ReproduceAbstract
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.