ArticleJournal of applied crystallography2022
A semi-supervised deep-learning approach for automatic crystal structure classification.
Article in Journal of applied crystallography, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Rust-accelerated powder X-ray diffraction simulation for high-throughput and machine-learning-driven materials science.Journal of applied crystallography · 2026Article
- Prediction of the space group and cell volume by training a convolutional neural network with primitive 'ideal' diffraction profiles and its application to 'real' experimental data.Journal of applied crystallography · 2025Article
- Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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Authors and funding
5 authors.
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Abstract
The structural solution problem can be a daunting and time-consuming task. Especially in the presence of impurity phases, current methods, such as indexing, become more unstable. In this work, the novel approach of semi-supervised learning is applied towards the problem of identifying the Bravais lattice and the space group of inorganic crystals. The reported semi-supervised generative deep-learning model can train on both labeled data,
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