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This study introduces an automated machine learning program for scanning tunneling spectroscopy (STS) tip conditioning. It reliably identifies suitable tip conditions for high-quality electronic structure characterization of nanomaterials.

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Area of Science:

  • Surface science
  • Nanomaterials characterization
  • Scanning probe microscopy (SPM)

Background:

  • Scanning tunneling spectroscopy (STS) is crucial for electronic structure analysis of nanomaterials.
  • Tip condition significantly impacts STS data quality and reliability.
  • Manual tip conditioning is time-consuming and can introduce variability.

Purpose of the Study:

  • To develop an automated machine learning (ML) program for STS tip conditioning.
  • To enable reliable identification of the Au(111) Shockley surface state in dI/dV spectra.
  • To minimize user intervention in tip preparation for STM studies.

Main Methods:

  • Utilized a height-based segmentation algorithm for STM topographic image analysis.
  • Trained ML models on 1789 archived dI/dV spectra to assess tip condition.
  • Employed decision tree ensemble, boosting models, and deep neural networks (DNNs).

Main Results:

  • The ML program reliably identifies suitable tip conditions for STS.
  • Automated tip conditioning was demonstrated on clean and sparsely covered gold surfaces.
  • The system accurately identifies the Au(111) Shockley surface state.

Conclusions:

  • The developed automated program enhances reproducibility and reduces operational costs in surface science.
  • This ML-driven approach accelerates nanomaterial discovery and characterization via STM.
  • The methodology is adaptable for tip conditioning on various substrates.