Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy
Abstract
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of humanmore »
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1857888
- Alternate Identifier(s):
- OSTI ID: 1813222
- Grant/Contract Number:
- 34532; AC05-00OR22725
- Resource Type:
- Published Article
- Journal Name:
- ACS Nano
- Additional Journal Information:
- Journal Name: ACS Nano Journal Volume: 15 Journal Issue: 8; Journal ID: ISSN 1936-0851
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; machine learning; artificial intelligence; automated experiments; scanning probe microscopy; reinforcement learning; Gaussian process regression; Bayesian optimization; scanning transmission electron microscopy; autonomous; imaging; algorithms; neural networks; mathematical methods
Citation Formats
Kalinin, Sergei V., Ziatdinov, Maxim, Hinkle, Jacob, Jesse, Stephen, Ghosh, Ayana, Kelley, Kyle P., Lupini, Andrew R., Sumpter, Bobby G., and Vasudevan, Rama K. Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy. United States: N. p., 2021.
Web. doi:10.1021/acsnano.1c02104.
Kalinin, Sergei V., Ziatdinov, Maxim, Hinkle, Jacob, Jesse, Stephen, Ghosh, Ayana, Kelley, Kyle P., Lupini, Andrew R., Sumpter, Bobby G., & Vasudevan, Rama K. Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy. United States. https://doi.org/10.1021/acsnano.1c02104
Kalinin, Sergei V., Ziatdinov, Maxim, Hinkle, Jacob, Jesse, Stephen, Ghosh, Ayana, Kelley, Kyle P., Lupini, Andrew R., Sumpter, Bobby G., and Vasudevan, Rama K. Fri .
"Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy". United States. https://doi.org/10.1021/acsnano.1c02104.
@article{osti_1857888,
title = {Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy},
author = {Kalinin, Sergei V. and Ziatdinov, Maxim and Hinkle, Jacob and Jesse, Stephen and Ghosh, Ayana and Kelley, Kyle P. and Lupini, Andrew R. and Sumpter, Bobby G. and Vasudevan, Rama K.},
abstractNote = {Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Finally, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.},
doi = {10.1021/acsnano.1c02104},
journal = {ACS Nano},
number = 8,
volume = 15,
place = {United States},
year = {Fri Jul 16 00:00:00 EDT 2021},
month = {Fri Jul 16 00:00:00 EDT 2021}
}
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