Machine learning for polyphenol-based materials

Abstract

Polyphenol-based materials, primarily composed of polyphenolic compounds, have attracted considerable attention due to their unique chemical structures and biological activities. However, there are many derivatives of polyphenols, resulting in the complexity and diversity of polyphenol-based materials. Traditional methods are difficult to meet the rapid development of polyphenol-based materials. Machine learning, known for its proficiency in predicting performance, optimizing synthesis processes, and designing novel materials, offers significant potential in the intelligent design and applications of polyphenol-based materials. In this review, we summarize the recent advancements in the research and development of polyphenol-based materials and machine learning. The intersection of polyphenol-based materials and machine learning is also discussed, including their applications in biomedical, environmental, and energy fields. The challenges and prospects for the future development of polyphenol-based materials based on machine learning are highlighted.