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Inversion of simulated positron annihilation lifetime spectrum using a neural network.

V C Viterbo1, J P Braga, A P Braga

  • 1Departamento de Química-ICEx, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG, Brasil.

Journal of Chemical Information and Computer Sciences
|March 30, 2001
PubMed
Summary

This study introduces a neural network Hopfield model for positron annihilation lifetime spectroscopy inversion. The model accurately reconstructs lysozyme density functions, achieving high precision with 64 neurons.

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Area of Science:

  • Physics
  • Computational Chemistry
  • Spectroscopy

Background:

  • Positron annihilation lifetime spectroscopy (PALS) is a sensitive technique for probing free volume in materials.
  • Inverting PALS data to obtain material density functions can be challenging due to inherent data complexities.
  • Developing robust computational methods is crucial for accurate PALS data analysis.

Purpose of the Study:

  • To present a novel method for inverting positron annihilation lifetime spectroscopy data using a neural network Hopfield model.
  • To evaluate the precision and accuracy of the neural network model in reconstructing density functions.
  • To investigate the influence of network parameters, such as the number of neurons and learning time, on inversion accuracy.

Main Methods:

  • A simulated PALS spectrum was generated from a known lysozyme density function, free from experimental noise and resolution effects.

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  • A neural network Hopfield model was trained using this simulated spectrum as the exact target data.
  • The performance of the trained neural network was assessed by comparing its inverted density function with the original exact function.

    • Main Results:

    • The neural network Hopfield model demonstrated a fair agreement with the exact density function.
    • The precision of the inverted density function was found to be dependent on the number of neurons and learning time.
    • Specifically, achieving a percentual relative error of 0.4% for the maximum of the density function was possible with 64 neurons.

    Conclusions:

    • The developed neural network Hopfield model offers a promising approach for the inversion of positron annihilation lifetime spectroscopy data.
    • The model's precision is influenced by its architecture and training parameters, highlighting the importance of optimization.
    • This method provides a viable pathway for accurate characterization of material properties through PALS data analysis.