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Partial radial distribution functions for a two-component glassy solid, GeSe , from scattering experimental data

Felipe Silva Carvalho1, João Pedro Braga2

  • 1Departamento de Química - ICEx, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG, Brazil. felipe.s.carvalho_qui@hotmail.com.

Journal of Molecular Modeling
|March 24, 2022
PubMed
Summary
This summary is machine-generated.

The Hopfield neural network method was extended to analyze complex glassy solids, successfully retrieving structural information like the radial distribution function from experimental data. This robust approach refines inverse problem-solving for materials science.

Keywords:
Glassy solidHopfield neural networkIll-posed problemsRadial distribution functionScattering experimental data

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Area of Science:

  • Materials Science
  • Computational Physics
  • Machine Learning

Background:

  • The Hopfield neural network (HNN) is effective for inverse problems in monoatomic liquids.
  • Retrieving structural functions like g(r) and C(r) from scattering data is crucial for materials characterization.

Purpose of the Study:

  • To extend the HNN method for analyzing complex two-component glassy solids, specifically GeSe3.
  • To validate the HNN's capability in accurately determining structural properties of more intricate materials.

Main Methods:

  • Applied the Hopfield neural network to solve coupled equations for a GeSe3 glassy solid.
  • Iteratively adjusted initial conditions based on previous run results to refine calculations.
  • Utilized experimental scattering data as input for the network.

Main Results:

  • Successfully retrieved the radial distribution function (g(r)) for the complex GeSe3 system.
  • Achieved accurate peak intensities and large-r behavior by refining calculations.
  • Demonstrated the HNN's robustness in handling more complex material structures.

Conclusions:

  • The Hopfield neural network method is a robust tool for analyzing the structure of complex glassy solids.
  • The iterative adjustment of initial conditions enhances accuracy for intricate systems.
  • This approach advances the application of neural networks in materials structure determination from experimental data.