Machine learning for automated experimentation in scanning transmission electron microscopy

Kalinin, Sergei V.; Mukherjee, Debangshu; Roccapriore, Kevin; Blaiszik, Benjamin J.; Ghosh, Ayana; Ziatdinov, Maxim A.; Al-Najjar, Anees; Doty, Christina; Akers, Sarah; Rao, Nageswara S.; Agar, Joshua C.; Spurgeon, Steven R.

doi:10.1038/s41524-023-01142-0

Title: Machine learning for automated experimentation in scanning transmission electron microscopy

Journal Article · 19 December 2023 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-023-01142-0 · OSTI ID:2246971

; Mukherjee, Debangshu; Roccapriore, Kevin;

;

; Al-Najjar, Anees; Doty, Christina;

; Rao, Nageswara S.;

;

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: National Science Foundation; USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC05-00OR22725; AC05-76RL01830; SC0002501

OSTI ID:: 2246971

Report Number(s):: PNNL-SA-183322; 227; PII: 1142

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 9; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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