About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
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Enhancing the Accessibility of Machine Learning-Enabled Experiments
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| Presentation Title |
Pycroscopy, AEcroscopy, and data workflows: integrating customized control, data analysis and workflows in an autonomous microscopy facility |
| Author(s) |
Rama Krishnan Vasudevan, Jawad Chowdhury, Narasimha Ganesh, Zijie Wu, Stephen Jesse, Gerd Duscher, Yongtao Liu |
| On-Site Speaker (Planned) |
Rama Krishnan Vasudevan |
| Abstract Scope |
Scanning probe microscopy (SPM) and electron microscopy provide micro-scale and atomic-scale characterization of structural features of samples alongside functional characterization. However, integrating microscopes into the self-driving laboratories of the future is generally difficult, because microscopy control is often hampered by unavailability of suitable interfaces. Here, we present autonomous workflows developed through our pycroscopy and AEcroscopy ecosystem of python packages. AEcroscopy (Automated Experiments for Microscopy) works by combining a hardware layer using a field-programmable gate array (FPGA) device that can output customized waveforms, driven by a Labview-based executable, alongside a fully python-based package that the user can program in. Data acquired through AEcroscopy is standardized through pycroscopy's sidpy package, enabling full metadata extraction as well as providing a ready data model for ingestion into databases. Examples of autonomous SPM experiments using AECroscopy will be shown. The potential extensions to enable compatibility with bluesky will be discussed. |