A machine learning ensemble approach for predicting solar-sensitive hybrid photocatalysts on hydrogen evolution

• Published in: Physica scripta Vol. 99; no. 7; pp. 76015 - 76024
• Main Authors: Bakır, Rezan, Orak, Ceren, Yüksel, Aslı
• Format: Journal Article
• Language: English
• Published: IOP Publishing 01-07-2024
• Subjects: DBU; energy; hydrogen; machine learning; photocatalysis
• DOI: 10.1088/1402-4896/ad562a
• EISSN: 1402-4896
• ISSN: 0031-8949

Hydrogen, as the lightest and most abundant element in the universe, has emerged as a pivotal player in the quest for sustainable energy solutions. Its remarkable properties, such as high energy density and zero emissions upon combustion, make it a promising candidate for addressing the pressing challenges of climate change and transitioning towards a clean and renewable energy future. In an effort to improve efficiency and reduce experimental costs, we adopted machine learning techniques in this study. Our focus turned to predictive analyses of hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO 3 (GLFO), graphene-supported LaRuO 3 (GLRO), and graphene-supported BiFeO 3 (GBFO), examining their correlation with varying levels of pH, catalyst amount, and H 2 O 2 concentration. To achieve this, a diverse range of machine learning models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, and AdaBoost—each bringing its strengths to the predictive modeling arena. An important step involved combining the most effective models—Random Forests, Gradient Boosting, and XGBoost—into an ensemble model. This collaborative approach aimed to leverage their collective strengths and improve overall predictability. The ensemble model emerged as a powerful tool for understanding photocatalytic hydrogen evolution. Standard metrics were employed to assess the performance of our ensemble prediction model, encompassing R squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The yielded results showcase exceptional accuracy, with R squared values of 96.9%, 99.3%, and 98% for GLFO, GBFO, and GLRO, respectively. Moreover, our model demonstrates minimal error rates across all metrics, underscoring its robust predictive capabilities and highlighting its efficacy in accurately forecasting the intricate relationships between GLFO, GBFO, and GLRO values and their influencing factors.