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Finding Features from Microscopes to Simulations Via Ensemble Learning and Atomic Manipulation
Published online by Cambridge University Press: 22 July 2022
Abstract
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- Type
- Artificial Intelligence, Instrument Automation, And High-dimensional Data Analytics for Microscopy and Microanalysis
- Information
- Copyright
- Copyright © Microscopy Society of America 2022
References
Ghosh, A., Sumpter, B.G.., Dyck, O., Kalinin, S. V., Ziatdinov, M., npj Comput. Mater., 2021 7, 1.Google Scholar
Ghosh, A., Ziatdinov, M., Dyck, O., Sumpter, B. G., Kalinin, S. V., arXiv preprint, 2021. arXiv:2109.04541.Google Scholar
Ziatdinov, M., Ghosh, A., Wong, T., Kalinin, S.V., arXiv preprint, 2021. arXiv:2105.07485.Google Scholar
This effort (machine learning) is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences Data, Artificial Intelligence Nanoscale Science Research (NSRC AI) Centers program (A.G., BGS, S.V.K.). The STEM experiments were supported by the DOE, Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (O.D.) and by the DOE, Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (K.M.R., S.V.K.). Work was performed and partially supported (M.Z) at Oak Ridge National Laboratory's Center for Nanophase Materials Sciences (CNMS), a DOE Office of Science User Facility. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE).Google Scholar
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