Artificial Intelligence-Assisted Design and Optimization of BioNanocomposites for Biomedical and Environmental Applications

Bio-nanocomposites, produced from renewable resources, stand out as one of the promising next-generation material classes for both biomedical and environmental applications due to their biocompatible and biodegradable structures.However, the vast and complex design space of these materials, which depends on numerous variables including the type of biopolymer matrix and the type and concentration of the nanostructured filler, makes their optimization quite challenging with traditional “trial-and-error” based research methods.This study provides a general overview of an artificial intelligence (AI)-based methodology that holds the potential to overcome this complexity.In this context, the limitations of the traditional “forward screening” approach are discussed, and the “inverse design” paradigm, based on the idea of “designing a material with desired properties,” is proposed as a solution.The study outlines the potential roles of various AI tools in this field, from supervised learning models to deep learning approaches and optimisation algorithms.This theoretical framework is substantiated by practical case studies that range from drug delivery to sustainable packaging.The case studies demonstrate how AI not only enables the prediction of properties but also facilitates strategically developing materials for a particular function, thereby accelerating the discovery process.Finally, current challenges, such as data scarcity and the interpretability of models, are addressed, and future visions to overcome these obstacles, including physics-informed AI, autonomous discovery platforms, and human-AI partnerships, are discussed.It is concluded that this synergy has the capacity to stimulate a new era of innovation in sustainable materials science.