Machine Learning-Assisted Prediction of Photothermal Metal-Phenolic Networks

Photothermal therapy (PTT) demonstrates significant potential in cancer treatment, wound healing, and antibacterial therapy, with its efficacy largely depending on the performance of photothermal agents (PTAs). Metal-phenolic network (MPN) materials are ideal PTA candidates due to their low cost, good biocompatibility and excellent ligand-to-metal charge transfer properties. However, not all MPNs exhibit significant photothermal properties, and the vast chemical space of MPNs (over 700,000 potential combinations) complicates the screening of high-photothermal materials. To address this challenge, this study introduces machine learning (ML) methods for predicting the photothermal performance of MPNs. A database of photothermal properties of 80 modular MPNs was constructed, and the ML process was optimized through feature engineering and model training. The selected extreme gradient boosting model (XGBoost) successfully identified 1,654 high photothermal MPNs from a virtual database of 44,438. Subsequent experimental validation revealed a remarkable success rate of 70 % in predicting high photothermal MPNs. Additionally, several previously unreported high photothermal MPNs were discovered, demonstrating advantages in photothermal antibacterial applications. This study offers an innovative ML-driven approach for the efficient screening of MPN materials, providing a solid foundation for PTA design in PTT and other biomedical applications.