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Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation.

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Titel: Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation.
Autoren: Liu, Yu, Pratiush, Utkarsh, Bemis, Jason, Proksch, Roger, Emery, Reece, Rack, Philip D., Liu, Yu-Chen, Yang, Jan-Chi, Udovenko, Stanislav, Trolier-McKinstry, Susan, Kalinin, Sergei V.
Quelle: Review of Scientific Instruments; Sep2024, Vol. 95 Issue 9, p1-10, 10p
Schlagwörter: MACHINE learning, SCIENTIFIC discoveries, OPERATIONAL definitions, COMPUTERS, MICROSCOPES, PYTHON programming language
Abstract: The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning. [ABSTRACT FROM AUTHOR]
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Abstract:The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning. [ABSTRACT FROM AUTHOR]
ISSN:00346748
DOI:10.1063/5.0219990