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A Semi-Supervised Approach for Automatic Crystal Structure Classification



Satvik Lolla, Haotong Liang, A. Gilad Kusne, Ichiro Takeuchi, William D. Ratcliff


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, we apply the novel approach of semi-supervised learning towards the problem of identifying the Bravais lattice and the space group of inorganic crystals. Our semi-supervised generative deep learning model can use both labeled and unlabeled data during training. This approach allows our models to take advantage of the troves of unlabeled data that current approaches cannot. This should result in a better transfer to real data. In this work, we classify powder diffraction patterns into all 14 Bravais lattices and 144 space groups (we limit the number due to sparse coverage in crystal structure databases), which covers more crystal classes than other studies. Our models also drastically outperform current deep learning approaches for both space group and Bravais Lattice classification using less training data.
Journal of Applied Crystallography


neutron, artificial intelligence, crystallography


Lolla, S. , Liang, H. , Kusne, A. , Takeuchi, I. and Ratcliff, W. (2021), A Semi-Supervised Approach for Automatic Crystal Structure Classification, Journal of Applied Crystallography (Accessed April 17, 2024)
Created November 1, 2021, Updated November 29, 2022