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

arXiv: https://arxiv.org/abs/1911.07644 · HTML: https://mlsanity.com/paper/1911.07644/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Overhead-MNIST: Machine Learning Baselines for Image Classification](https://mlsanity.com/paper/2107.00436/) (2021)

## Abstract (arXiv)

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.