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Adsorbate-adsorbent potential energy function from second virial coefficient data: a non-linear Hopfield Neural Network approach

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Abstract

The Hopfield Neural Network has been successfully applied to solve ill-posed inverse problems in different fields of chemistry and physics. In this work, the non-linear approach for this method will be applied to retrieve the empirical parameters of potential energy function, \(E_{p}(r)\), between adsorbate and adsorbent from experimental data. Since the adsorption data is related to the second virial coefficient and therefore to \(E_{p}(r)\) through an integral equation, the Hopfield Neural Network will be used to find the best parameters which fits the experimental data. Initially simulated results will be analyzed to verify the method performance for data sets with and without noise addition. Then, experimental data for adsorption of propionitrile on activated carbon will be treated. Results presented here corroborate to the robustness of this method.

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Data availability

The data sets and codes generated during this work are available from the corresponding author on reasonable request.

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Funding

This work received financial support from CNPq.

Author information

Authors and Affiliations

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

    Felipe Silva Carvalho & João Pedro Braga

  2. Departamento de Química, CEFET, Contagem, Minas Gerais, Brazil

    Márcio Oliveira Alves

Authors
  1. Felipe Silva Carvalho
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  2. João Pedro Braga
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  3. Márcio Oliveira Alves
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Felipe Silva Carvalho and Márcio Oliveira Alves. The code used was developed by João Pedro Braga and adapted to this work by Felipe Silva Carvalho. The first draft of the manuscript was written by Felipe Silva Carvalho and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to João Pedro Braga.

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The authors declare no competing interests.

Additional information

This article belongs to the Topical Collection: XXI-Brazilian Symposium of Theoretical Chemistry (SBQT2021)

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Carvalho, F.S., Braga, J.P. & Alves, M.O. Adsorbate-adsorbent potential energy function from second virial coefficient data: a non-linear Hopfield Neural Network approach. J Mol Model 28, 286 (2022). https://doi.org/10.1007/s00894-022-05274-w

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  • DOI: https://doi.org/10.1007/s00894-022-05274-w

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