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

Yu Liu¹, Utkarsh Pratiush¹, Jason Bemis²

¹Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA.

The Review of Scientific Instruments

|September 16, 2024

Summary

This summary is machine-generated.

We developed a Python interface for automated scanning probe microscopy (SPM) control, enabling machine learning-driven scientific discovery. This infrastructure supports both routine tasks and advanced autonomous research.

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Area of Science:

Materials Science
Nanotechnology
Computational Science

Background:

Scanning Probe Microscopy (SPM) is crucial for nanoscale characterization.
Automating SPM operations can accelerate scientific discovery.
Integrating machine learning (ML) with SPM requires robust computational infrastructure.

Purpose of the Study:

To create a Python interface for controlling SPM systems.
To enable the use of high-performance computing for ML-driven SPM workflows.
To establish a platform for automating scientific discovery using SPM.

Main Methods:

Development of a Python interface library for SPM control.
Integration with local and remote high-performance computing resources.
Implementation of a platform for abstracting SPM operations into ML workflows.

Main Results:

A functional Python interface enabling seamless SPM control.
Demonstrated capability to leverage high computation power for ML algorithms.
A versatile platform supporting automated SPM workflows.

Conclusions:

The developed infrastructure facilitates automated SPM operations.
This system enables autonomous scientific discovery through machine learning.
The platform supports both routine SPM tasks and advanced research.