Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

Ziatdinov, Maxim A.; Yaman, Muammer Yusuf; Liu, Yongtao; Ginger, David; Kalinin, Sergei V.

doi:10.1039/D3DD00196B

Title: Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

Journal Article · 12 June 2024 · Digital Discovery

DOI: https://doi.org/10.1039/D3DD00196B · OSTI ID:2438145

Ziatdinov, Maxim A. ^[1];

^[2]; Liu, Yongtao ^[3]; Ginger, David ^[2]; Kalinin, Sergei V. ^[4]

Physical Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Department of Chemistry, University of Washington, Seattle, WA, 98195, USA
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Physical Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA, Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA

ss-rVAE classification can generalize from a small labeled data subset with weak orientational disorder to a larger unlabeled dataset with stronger disorder. We apply it to nanoparticle datasets to train a robust classifier and understand physical factors of data variation.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NONE; SC0021118; SC0019288

OSTI ID:: 2438145

Journal Information:: Digital Discovery, Journal Name: Digital Discovery Journal Issue: 6 Vol. 3; ISSN DDIIAI; ISSN 2635-098X

Publisher:: Royal Society of Chemistry (RSC)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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