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How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

Agnieszka Pregowska¹, Agata Roszkiewicz¹, Magdalena Osial¹

¹Department of Information and Computational Science, Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland.

Microscopy Research and Technique

|June 12, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) and quantum computing (QC) can enhance scanning probe microscopy (SPM) by automating experiments and improving accuracy. This research explores AI-QC-powered SPM, identifying a research gap and future directions.

Keywords:

artificial intelligence automated experiments machine learning quantum computation scanning probe microscopy

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Area of Science:

Materials Science
Computational Science
Nanotechnology

Background:

Scanning Probe Microscopy (SPM) is crucial for materials characterization but faces challenges like long scan times and sample damage.
Artificial Intelligence (AI) and Quantum Computing (QC) offer potential solutions to these limitations.

Purpose of the Study:

To explore the integration of AI and QC to support and enhance SPM measurements.
To identify research gaps and outline future directions for AI-QC-powered SPM.

Main Methods:

Focus on AI-based algorithms, particularly Machine Learning, and their application to SPM.
Investigate the synergistic potential of combining AI with Quantum Computing (QC) for SPM enhancement.
Discuss limitations of the AI-QC approach for SPM.

Main Results:

AI can automate SPM experiments, optimize sample region selection, and elucidate structure-property relationships, increasing efficiency and accuracy.
The combination of AI and QC shows significant potential for advancing SPM practical applications.
Limitations of the AI-QC-SPM approach were identified.

Conclusions:

AI and QC integration presents a promising research path for improving SPM capabilities.
Further research is needed to fully realize the potential of AI-QC-powered SPM.
This work highlights a research gap in advancing SPM through computational methods.