Determination of bandgap of period 3, 4, and 5 transition metal dopants on zinc oxide using an artificial neural network based approach

Abstract

Artificial intelligence (AI) and machine learning (ML) have rapidly emerged as valuable tools for chemical research, offering new ways to analyze and understand complex chemical systems. This research article investigates the use of adaptive neuro-fuzzy inference system (ANFIS) and multi-layer perceptron (MLP) models to predict the bandgap of transition metal doped zinc oxide (ZnO). The opto-electronic properties of transition metal doped ZnO complexes are of significant interest because of their applications is optoelectronic systems. The MLP and ANFIS models were trained using a dataset of experimentally measured bandgap values and the corresponding structural parameters of the doped ZnO systems. The performance of the models was evaluated using statistical metrics i.e., RMSE, R, and MAE. The results showed that both MLP and ANFIS models were capable of accurately predicting the bandgap of transition metal doped ZnO. However, the ANFIS model demonstrated superior performance with higher accuracy and better generalization ability. The study provides a useful approach for predicting the bandgap of transition metal doped ZnO using machine learning techniques and may contribute to the development of advanced optoelectronic devices.