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Mathematical modeling for the potential energy of the aminophenol derivative azomethine molecule via Bezier surfaces and fuzzy inference system

  • Temel Ermiş EMAIL logo
Published/Copyright: November 12, 2024

Abstract

In this study, we have managed to model the energy surface of the aminophenol derivative azomethine molecule mathematically depending on two torsion angles SC1(C9C10C12N14) and SC2(C2C1C6C11). For this purpose, firstly, discrete data obtained from Density Functional Theory calculations have been converted into continuous data with the help of the Fuzzy Inference System. Thus, it is possible to calculate energy values for untested data, which are very costly in terms of time to obtain with other methods/experiments. Then, the continuous and non-smooth surface obtained from the fuzzy inference system and representing the energy values of the molecule has been transformed into a differentiable surface with the help of Bezier surfaces. Thus, an objective function has been obtained in which global optimization methods based on the derivative (or gradient) operator could be used.

2010 MSC: 65D05; 65D17; 90C26; 03B52

Corresponding author: Temel Ermiş, Department of Mathematics and Computer Sciences, Eskisehir Osmangazi University, 26040, Eskisehir, Türkiye, E-mail: 

Acknowledgments

The author would like to thank Emel Ermiş from Eskişehir Technical University for sharing the DFT results in [3] and providing support with expert opinions while constructing the rule base of the fuzzy inference system. Additionally, the author sincerely thanks Ahmet Şahiner and Nurullah Yılmaz from Süleyman Demirel University for their contributions in the implementation of the method given in [11].

  1. Research ethics: We confirm that this research is in compliance with ethical standards and is not currently being considered for publication elsewhere.

  2. Informed consent: All authors consent to participate in this work.

  3. Author contributions: Conceptualization, Methodology, Investigation, Validation, Visualization, Resources, Formal analysis, Writing – review & editing.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors declare that they have no conflict of interest.

  6. Research funding: The manuscript has been not financially supported.

  7. Data availability: The data that support this research are available in the manuscript.

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Received: 2024-08-21
Accepted: 2024-10-22
Published Online: 2024-11-12
Published in Print: 2025-03-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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