Data-driven approaches to study the spectral properties of chemical structures
Ibtisam Masmali1, Muhammad Faisal Nadeem2, Zeeshan Saleem Mufti3
1Department of Mathematics, College of Science, Jazan University, Jazan, 45142, Saudi Arabia.
Heliyon
|September 18, 2024
Summary
Machine learning predicts molecular properties like energy and Estrada index for bismuth tri-iodide and benzene compounds. This data-driven approach enhances understanding of spectral theory in conjugated hydrocarbons.
Area of Science:
- Computational chemistry
- Materials science
- Graph theory
Background:
- Molecular energy, derived from eigenvalues, is key for conjugated hydrocarbon π-electron energy.
- Understanding spectral properties of materials like bismuth tri-iodide and benzene is crucial.
Purpose of the Study:
- To apply machine learning for calculating spectral properties of molecular graphs.
- To develop general equations for predicting molecular energy, inertia, nullity, signature, and Estrada index.
- To analyze bismuth tri-iodide and benzene rings in 2D networks.
Main Methods:
- Utilized machine learning techniques for property calculation.
- Employed MATLAB for eigenvalue extraction from data.
- Developed and applied general equations for property estimation.
- Performed graphical analysis for comparison of estimated and actual values.
Main Results:
- Successfully calculated energy, inertia, nullity, signature, and Estrada index for specified molecular graphs.
- Established general equations for these molecular properties.
- Demonstrated strong correlation between estimated and actual values via graphical analysis.
Conclusions:
- Data-driven techniques show significant potential for predicting molecular properties.
- This study enhances the application of spectral theory in materials science.
- Machine learning offers a powerful tool for computational chemistry research.
Keywords:
Benzene ringBismuth tri-iodideData-driven methodologiesEigenvaluesEnergyMachine learningPredictive modelingMore Related Videos
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