Towards calibration-invariant spectroscopy using deep learning.

Area of Science:

• Spectroscopy
• Materials Science
• Artificial Intelligence

Background:

• Spectrometer calibration drift and inter-instrument variability hinder accurate comparison of spectral data.
• Manual feature extraction or qualitative analysis are current methods to address calibration issues.
• Accurate spectral analysis is crucial for understanding material properties, particularly in battery research.

Purpose of the Study:

• To develop an automated method for classifying electronic environments in manganese compounds using deep learning.
• To overcome challenges posed by spectrometer calibration differences in electron energy loss spectroscopy (EELS).
• To enable reliable comparison of spectral data across different instruments and measurement times.

Main Methods:

• Utilized deep convolutional neural networks (CNNs) for automated spectral feature extraction.
• Trained models on 2001 electron energy loss spectroscopy (EELS) spectra of manganese compounds.
• Developed and tested a novel fully convolutional neural network architecture with enhanced translation-invariance.

Main Results:

• Successfully classified three distinct electronic environments of manganese with high accuracy.
• Demonstrated the model's robustness and immunity to calibration differences.
• Validated performance on spectra from different instruments, confirming generalizability.

Conclusions:

• Deep convolutional neural networks offer a powerful automated solution for spectral analysis in EELS.
• The proposed translation-invariant architecture effectively addresses spectrometer calibration variability.
• This method facilitates reliable and comparable material characterization for applications like battery development.