Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning.

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ID: 104857
2019
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Abstract
While machine learning has been making enormous strides in many technical areas, it is still massively underused in transmission electron microscopy. To address this, a convolutional neural network model was developed for reliable classification of crystal structures from small numbers of electron images and diffraction patterns with no preferred orientation. Diffraction data containing 571,340 individual crystals divided among seven families, 32 genera, and 230 space groups were used to train the network. Despite the highly imbalanced dataset, the network narrows down the space groups to the top two with over 70% confidence in the worst case and up to 95% in the common cases. As examples, we benchmarked against alloys to two-dimensional materials to cross-validate our deep-learning model against high-resolution transmission electron images and diffraction patterns. We present this result both as a research tool and deep-learning application for diffraction analysis.
Reference Key
aguiar2019decodingscience Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Aguiar, J A;Gong, M L;Unocic, R R;Tasdizen, T;Miller, B D;
Journal Science advances
Year 2019
DOI
10.1126/sciadv.aaw1949
URL
Keywords
genetic algorithm qsar hepatotoxicity counter-propagation artificial neural networks imbalanced dataset

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