Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems

Antimicrobial resistance and biofilm-associated contamination continue to pose critical challenges in food safety and biomedical applications, necessitating the development of advanced antimicrobial materials with enhanced efficacy, safety, and functional adaptability. Antimicrobial nanomaterials offer versatile solutions due to their tunable physicochemical properties, surface engineering capabilities, and controlled release behaviors, enabling improved antimicrobial and antibiofilm performance across diverse systems. This review highlights the main advancements in AI-assisted design of antimicrobial nanomaterials, demonstrating how data-driven approaches are increasingly used to predict antimicrobial activity, optimize synthesis parameters, model nanotoxicity, integrate multimodal datasets, and improve interpretability through explainable AI frameworks. Key findings indicate that machine learning-guided strategies and autonomous experimental platforms significantly accelerate material optimization while reducing reliance on traditional trial-and-error methods. The review further summarizes the performance and mechanisms of major antimicrobial nanomaterial systems, including metal and metal oxide nanoparticles, metal-organic frameworks, polymeric nanocarriers, nanoemulsions, and hybrid nanostructures, with emphasis on their translational applications in food preservation, antimicrobial coatings, wound healing, implant protection, and drug delivery. Despite these advances, challenges remain in data quality, model generalizability, toxicity prediction, reproducibility, and regulatory translation. AI-enabled and data-driven frameworks provide a powerful pathway for accelerating the rational design and practical implementation of next-generation antimicrobial nanomaterials.