Bayesian active learning with model selection for spectral experiments.

Area of Science:

• Chemistry
• Spectroscopy
• Computational Chemistry

Background:

• Active learning enhances spectral experiment efficiency.
• Model selection and parameter estimation are crucial for spectral data analysis.
• Existing methods may lack efficiency in complex spectral analyses.

Purpose of the Study:

• To develop an active learning method integrating model selection with parameter estimation.
• To utilize multiple parametric models within an active learning framework.
• To improve the efficiency and accuracy of spectral data analysis, specifically for X-ray photoelectron spectroscopy.

Main Methods:

• Proposed an active learning strategy incorporating model selection.
• Employed multiple parametric models as learning models.
• Utilized Bayesian posterior distributions for active measurement of model selection and parameter estimation points.

Main Results:

• Demonstrated the effectiveness of the proposed active learning method.
• Successfully applied the method to spectral deconvolution tasks.
• Showcased utility in Hamiltonian selection for X-ray photoelectron spectroscopy.

Conclusions:

• The developed active learning with model selection approach is effective for spectral experiments.
• Bayesian posterior distribution guides efficient data acquisition for analysis.
• This method offers a significant advancement for analyzing complex spectral data, particularly in X-ray photoelectron spectroscopy.