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

These iterative optimization algorithms are computational expensive and difficult to converge. Unlike iterative optimization methods, supervised machine learning using two stage training-testing becomes a great advantage for fast real-time inference since the most expensive computations are performed during training. Deep Learning plays a very important role tackling these type of problems but requires large dataset to train the multi-layer model parameters of the network [1]. Here, we are interested in creating a molecular image dataset including shape images from real space and diffraction patterns from reciprocal space for machine learning practices. We call this dataset Molecular-MNIST because it consists 10 different size of molecules where each molecule has 2,000 structural variants - in an analogy of the famous 10-digit handwritten dataset MNIST [2]. 2. Molecular-MNIST Dataset 2.1.