Rapid Design of Efficient Mn3O4-Based Photocatalysts by Machine Learning and Density Functional Theory Calculations

The development of efficient photocatalysts for visible-light-driven pollutant degradation contributes to sustainable and green solutions to environmental challenges. However, optimizing catalyst composition and structure remains a costly and time-consuming process. Here, a comprehensive design strategy is presented for the fast development of efficient Al-doped Mn3O4-based photocatalysts, combining density functional theory (DFT), machine learning (ML), and laboratory experiments. DFT-calculated effective mass and bandgaps, serving as indicators of charge mobility and light harvesting, respectively, are employed as descriptors to determine the optimal Al dopant amount. Al0.5Mn2.5O4 is identified as a promising candidate due to its favorable bandgap and charge mobility. To further enhance performance, AlxMn3-xO4/Ag3PO4 heterojunctions are synthesized, leveraging ML to optimize the ratios between AlxMn3-xO4 and Ag3PO4. The best material is determined to be an Al0.5Mn2.5O4/35 wt%-Ag3PO4 composite, which exhibits a 27-fold increase in photocatalytic efficiency for methylene blue degradation under visible light compared to pristine Mn3O4. This study not only provided promising photocatalysts for practical pollutant degradation but highlighted the potential of computational and ML-guided approaches to accelerate photocatalyst discovery. These computational methods provide a framework for the rational design of advanced materials for environmental remediation applications.