A Molecular-MNIST Dataset for Machine Learning Study on Diffraction Imaging and Microscopy
arXiv:1911.07644v14 citations
Originality Synthesis-oriented
AI Analysis
This provides a domain-specific dataset for researchers in diffraction imaging and microscopy, but it is incremental as it adapts existing data generation methods.
The authors created a dataset of 10 molecules with 2,000 structural variants each from Molecular Dynamics trajectories to serve as a benchmark for machine learning in scattering, imaging, and microscopy.
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.