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.
| Original language | English |
|---|---|
| Article number | 227 |
| Journal | npj Computational Materials |
| Volume | 9 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2023 |
Funding
This research (D.M., K.R., A.G., M.Z., A.A., N.S.R.), is sponsored by the INTERSECT Initiative as part of the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. S.V.K. was supported by the UT Knoxville start-up funding. This research used resources of the Center for Nanophase Materials Sciences, which is a DOE Office of Science User Facility, at the Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725. C.D., S.A., and S.R.S. were supported by the Chemical Dynamics Initiative / Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830. J.C.A. acknowledges support from the Army/ARL via the Collaborative for Hierarchical Agile and Responsive Materials (CHARM) under cooperative agreement W911NF-19-2-0119, National Science Foundation under grant OAC:DMR:CSSI \u2013 2246463, and US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award No. DE-SC-0002501. B.J.B acknowledges support from financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Material Design (CHiMaD).