Numerical simulation for electrical conductivity of organic- inorganic hybrid compounds by artificial intelligence-based models
DOI:
https://doi.org/10.58916/jhas.v9i3.450Keywords:
Artificial Neural Network (ANN), Artificial-intelligence, Decision tree, Electrical conductivity, Modelling, Organic-inorganic compounds.Abstract
In this work, artificial intelligence-based approaches were applied to predict the electrical conductivity for organic-inorganic hybrid compounds, namely tritetrapropylammoniumdodeca chlorobismuthate(III) [(C3H7)4N]3Bi3Cl12 and bis (4-acetylaniline) tetrachlorocadmate [C8H10NO]2[CdCl4] with only the knowledge of the system temperature and frequency. The suggested machine learning methods are trained, tested, and validated using experimental datasets. These datasets were used as inputs to different machine learning algorithms; these implemented algorithms are artificial neural network-scaled conjugate gradient (ANN-SCG), artificial neural network-gradient descent (ANN-GD), and decision tree. In particular, all ANN models were optimised by adjusting the hyperparameters in order to produce a superior neural network architecture, which provides the lowest value of the gradient error. Upon comparison, it was found that all ANN models showed better accuracy and significant precision in demonstrating nonlinear relationships with the electrical conductivity dataset, leading to a better prediction of the simulated electrical conductivity with more than 99% accuracy for the presented data sets. The decision tree model could not predict the electrical conductivity with acceptable accuracy in terms of high RMSEs and low correlation coefficients. Based on the obtained results, it suggested that ANNs were quite efficient techniques for predicting nonlinear and complicated correlations between independent variables and the response parameter.
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