DEEP LEARNING APPROACHES FOR THE DEVELOPMENT OF INSECT- AND MOULD-RESISTANT PAINTS: AI-DRIVEN FORMULATION AND OPTIMIZATION

Paint coatings serve as the first line of defense against environmental degradation, yet microbial infestation and insect adhesion continue to pose significant challenges, leading to structural damage and health-related risks. Traditional antifungal and insect-repellent solutions often depend on chemical additives that raise environmental and health concerns. This study investigates the potential of deep learning to optimize paint formulations for enhanced resistance to mould and insect infestation. We introduce a novel AI-driven framework that integrates convolutional neural networks (CNNs) to detect microbial growth patterns, recurrent neural networks (RNNs) to model temporal environmental influences, and generative adversarial networks (GANs) to simulate and generate optimized paint formulations. The system is trained on a comprehensive dataset comprising spectral and microscopic imagery, chemical composition data, and environmental conditions. Our results indicate that the proposed deep learning models outperform conventional heuristic-based methods in identifying effective resistance-enhancing formulations. These findings underscore the transformative role of artificial intelligence in advancing material science and promote the development of eco-friendly, self-adaptive coatings. Future work will involve real-world testing and the integration of IoT-enabled sensors for dynamic resistance management.