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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Upon ionization, aromatic compounds generate a molecular ion that is observed as a prominent peak in their mass spectra. For example, the molecular ion peak for benzene appears at a mass-to-charge ratio of 78, while toluene is observed at a mass-to-charge ratio of 92. The molecular ion benzene is highly stable and does not readily undergo further fragmentation due to the significant amount of energy required to disrupt the aromatic stability of the benzene ring. In contrast, the molecular ion...
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Related Experiment Video

Updated: Oct 9, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
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Automatic determination of the spectrum-structure relationship by tree structure-based unsupervised and supervised

Shin Kiyohara1, Kakeru Kikumasa2, Kiyou Shibata2

  • 1Institute of Industrial Science, the University of Tokyo, Tokyo 153-8505, Japan; Laboratory for Materials and Structures, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan.

Ultramicroscopy
|December 16, 2021
PubMed
Summary

This study introduces a machine learning method for automatic spectrum interpretation, correlating spectral features with molecular structures. This approach aids in understanding spectral data for new materials, advancing spectroscopy applications.

Keywords:
ELNES/XANESInformaticsMachine learning

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Area of Science:

  • Spectroscopy
  • Computational Chemistry
  • Materials Science

Background:

  • Spectroscopy is crucial for analyzing chemical and bonding information.
  • Human interpretation of spectra, especially for novel materials, can be challenging.
  • Current methods rely on comparing experimental spectra with reference data.

Purpose of the Study:

  • To develop an automated, human-like interpretation method for spectral data using machine learning.
  • To establish reliable spectrum-structure relationships for unknown materials.
  • To enhance the understanding of spectral variations based on molecular properties.

Main Methods:

  • Combined unsupervised and supervised machine learning techniques.
  • Applied the method to a database of over 400 spectra from water and organic molecules.
  • Correlated spectral features with atomic, bond, and ligand descriptors.

Main Results:

  • Successfully identified correlations between spectral features (e.g., π* resonance in C-K edges) and molecular structures (e.g., multiple bonds).
  • Enabled automatic determination of physically and chemically sound spectrum-structure relationships.
  • Demonstrated the method's effectiveness for Electron Energy Loss Near Edge Structure (ELNES)/X-ray Absorption Near Edge Structure (XANES) spectra.

Conclusions:

  • The developed machine learning method provides an objective and data-driven approach to spectrum interpretation.
  • This technique facilitates a deeper understanding of how structural parameters influence spectral data.
  • The method is extensible to various spectroscopy types and complex datasets, including spatial and time-resolved data.