Materials discovery: understanding polycrystals from large-scale electron patterns


This paper explores the idea of modeling a large image data collection of polycrystal electron patterns, in order to detect insights in understanding materials discovery. There is an emerging interest in applying big data processing, management and modeling methods to scientific images, which often come in a form and with patterns only interpretable to domain experts. While large-scale machine learning approaches have demonstrated certain superiority in analyzing, summarizing, and providing an understandable route to data types like natural images, speeches and texts, scientific images is still a relatively unexplored area. Deep convolutional neural networks, despite their recent triumph in natural image understanding, are still rarely seen adapted to experimental microscopic images, especially in a large scale. To the best of our knowledge, we present the first deep learning solution towards a scientific image indexing problem using a collection of over 300K microscopic images. The result obtained is 54% better than a dictionary lookup method which is state-of-the-art in the materials science society.

In IEEE International Conference on Big Data (Big Data) 2016.
@inproceedings{liu2016materials, title={Materials discovery: understanding polycrystals from large-scale electron patterns}, author={Liu, Ruoqian and Agrawal, Ankit and Liao, Wei-keng and Choudhary, Alok and De Graef, Marc}, booktitle={2016 IEEE International Conference on Big Data (Big Data)}, pages={2261–2269}, year={2016}, organization={IEEE}}