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Adsorbate-adsorbent potential energy function from second virial coefficient data: a non-linear Hopfield Neural
Felipe Silva Carvalho1, João Pedro Braga2, Márcio Oliveira Alves3
1Departamento de Química - ICEx, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG, Brazil.
Journal of Molecular Modeling
|September 2, 2022
Summary
The Hopfield Neural Network effectively retrieves potential energy parameters from adsorption data. This robust method accurately fits experimental results, even with noisy datasets.
Area of Science:
- Physical Chemistry
- Computational Chemistry
- Materials Science
Background:
- Hopfield Neural Networks (HNN) are adept at solving ill-posed inverse problems.
- Potential energy functions are crucial for understanding adsorbate-adsorbent interactions.
- Adsorption data relates to the second virial coefficient and potential energy via integral equations.
Purpose of the Study:
- To apply a non-linear Hopfield Neural Network approach for empirical potential energy function parameter retrieval.
- To determine parameters governing adsorbate-adsorbent interactions from experimental adsorption data.
Main Methods:
- Utilizing a non-linear Hopfield Neural Network to solve the inverse problem.
- Relating adsorption data to the second virial coefficient and potential energy function through integral equations.
- Validating the method with simulated datasets, including those with added noise.
Main Results:
- The Hopfield Neural Network successfully retrieved empirical parameters for the potential energy function.
- The method demonstrated robustness and accuracy when applied to simulated data with and without noise.
- Experimental adsorption data for propionitrile on activated carbon was effectively analyzed.
Conclusions:
- The non-linear Hopfield Neural Network is a robust and effective tool for determining potential energy parameters from adsorption data.
- This computational approach offers a reliable method for analyzing experimental adsorption phenomena.
- The study validates the HNN's capability in addressing complex inverse problems in physical chemistry.
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