Artificial Intelligence-Driven Discovery and Optimization of Eco-Friendly Materials for Sustainable Engineering Applications

Abstract

The increasing pressure to find sustainable engineering solutions has upped the search to more sustainable materials to apply that would fit the environmental side of the requirements as well as the performance side. The study reports a new AI-GreenMatOpt framework that utilizes the capabilities of artificial intelligence to stimulate the discovery, classification, and optimization of environmentally sustainable materials. The suggested method combines the multi-modal deep learning procedure, generative inverse design, and evolutionary optimization to convert and examine heterogeneous sets of data about materials, such as structural images or physicochemical properties, and environmental indicators. Latent features are extracted and combined by a multi-modal autoencoder of the image-based and numerical inputs, allowing integrating material properties. These representations can then be applied in a generative model to approximate new materials with a high performance against eco-materials and an evolutionary algorithm that optimizes the designs based on a defined sustainability goal, i.e. low carbon footprint, biodegradability, and recyclability. The infrastructure provides a scalable and adaptive resource to material scientists and engineers who wish to move faster in creating green materials to serve present day engineering demands. The study helps to move further towards sustainable material science driven by AI and is also relevant to the entire world, helping to protect the environment and adopt more circular economy concepts in the industrial sector. The suggested AI-GreenMatOpt system achieves an overall accuracy of 98.4%.