A Machine Learning Approach to Determine the Band Gap Energy of High-Entropy Oxides Using UV-Vis Spectroscopy

This study introduces a machine learning-based framework for the automated determination of band gap energies in high-entropy oxides (HEOs) using UV-Vis spectroscopy data. Traditionally, band gap energies are obtained from the Tauc plots by manually extrapolating the linear region to the photon energy axis, a process that is time consuming and prone to human error, particularly when dealing with large datasets. To overcome these limitations, we developed a Python-based workflow that automates the band gap evaluation process through key steps, including data preprocessing, data augmentation, hyperparameter tuning, and band gap energy prediction. Various machine learning algorithms were employed to model the relationships between UV-Vis spectra and band gap energies, resulting in significant improvements in both accuracy and efficiency. Among the tested models, Bagging, Extra Trees, and Random Forest exhibited the best predictive performance, achieving mean absolute errors (MAE) as low as 0.26–0.28 eV and coefficients of determination (R2) of 0.73–0.74, substantially outperforming conventional automated methods. Although data augmentation and hyperparameter optimization yielded only modest performance gains, they contributed to improved model robustness. Overall, the proposed ML framework provides a scalable and efficient approach for the rapid characterization of HEOs, minimizing the need for manual analysis and accelerating data-driven materials discovery.