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Radial distribution function for liquid gallium from experimental structure factor: a Hopfield neural network

F S Carvalho1, J P Braga2

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

Journal of Molecular Modeling
|July 5, 2020
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Summary

A Hopfield neural network successfully retrieved the liquid gallium radial distribution function from experimental data. This powerful strategy accurately calculates atomic distribution, outperforming traditional methods.

Keywords:
Hopfield neural networkLiquid galliumRadial distribution functionStructure factor

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Area of Science:

  • Condensed Matter Physics
  • Computational Materials Science
  • Statistical Mechanics

Background:

  • Determining the radial distribution function (RDF) is crucial for understanding liquid structures.
  • Experimental structure factors provide essential data for RDF calculations.
  • Traditional methods like Fourier transforms and Monte Carlo simulations have limitations.

Purpose of the Study:

  • To investigate the efficacy of a Hopfield neural network (HNN) for retrieving the RDF of liquid gallium.
  • To compare HNN performance against established methods using experimental data.
  • To evaluate HNN's robustness under different initial conditions.

Main Methods:

  • Utilized a Hopfield neural network for the inverse problem of RDF calculation.
  • Employed experimental structure factor data for liquid gallium at 959 K.
  • Tested HNN with two distinct initial conditions: ideal gas RDF and a square-well potential gas simulation.

Main Results:

  • The Hopfield neural network accurately reproduced the radial distribution function of liquid gallium.
  • HNN results were comparable to those obtained via Fourier transform and Monte Carlo simulations.
  • Both tested initial conditions yielded accurate inverse results, demonstrating HNN's versatility.

Conclusions:

  • The Hopfield neural network is a powerful and accurate tool for calculating RDF from experimental structure factor data.
  • HNN offers a viable alternative to conventional methods for liquid structure analysis.
  • This approach enhances the ability to derive detailed atomic arrangements in liquid metals.