Masmali Ibtisam, Nadeem Muhammad Faisal, Mufti Zeeshan Saleem, Ahmad Ali, Koam Ali N A, Ghazwani Haleemah
Department of Mathematics, College of Science, Jazan University, Jazan, 45142, Saudi Arabia.
Department of Mathematics, COMSATS University Islamabad Lahore Campus, Lahore, 54000, Pakistan.
Heliyon. 2024 Sep 6;10(17):e37459. doi: 10.1016/j.heliyon.2024.e37459. eCollection 2024 Sep 15.
The molecular energy, which is the sum of all eigenvalues, is crucial in determining the total π-electron energy of conjugated hydrocarbon molecules. We used machine learning techniques to calculate the energy, inertia, nullity, signature, and Estrada index of molecular graphs for bismuth tri-iodide and benzene rings embedded in P-type surfaces within 2D networks. We applied MATLAB to extract the actual eigenvalues from the data and developed general equations for these molecular properties. We then used these equations to estimate the values and compared them to the actual values through graphical analysis. Our results demonstrate the potential of data-driven techniques in predicting molecular properties and enhancing our understanding of spectral theory.
分子能量是所有本征值之和，在确定共轭烃分子的总π电子能量方面至关重要。我们使用机器学习技术来计算二维网络中嵌入P型表面的三碘化铋和苯环的分子图的能量、惯性、零度、符号和 Estrada 指数。我们应用MATLAB从数据中提取实际本征值，并为这些分子性质建立了通用方程。然后，我们使用这些方程来估计值，并通过图形分析将它们与实际值进行比较。我们的结果证明了数据驱动技术在预测分子性质和增强我们对光谱理论理解方面的潜力。
