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 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. Overall, 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.
Original language | English |
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Pages (from-to) | 12604-12627 |
Number of pages | 24 |
Journal | ACS Nano |
Volume | 15 |
Issue number | 8 |
DOIs | |
State | Published - Aug 24 2021 |
Funding
This effort (ML and STEM) is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division (S.V.K., S.J., K.P.K., and A.R.L.), by U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Data, Artificial Intelligence and Machine Learning at DOE Scientific User Facilities program under award number 34532 (A.G. and B.G.S.), Artificial Intelligence Initiative (J.H.) at Oak Ridge National Laboratory and was performed and partially supported (M.Z., R.K.V.) at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.
Funders | Funder number |
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Artificial Intelligence Initiative | |
CNMS | |
Oak Ridge National Laboratory | |
Office of Basic Energy Sciences Data, Artificial Intelligence and Machine Learning | 34532 |
U.S. Department of Energy | |
Office of Science | |
Basic Energy Sciences | |
Oak Ridge National Laboratory | |
Division of Materials Sciences and Engineering |
Keywords
- Bayesian optimization
- Gaussian process regression
- artificial intelligence
- automated experiments
- autonomous
- machine learning
- reinforcement learning
- scanning probe microscopy
- scanning transmission electron microscopy