AI-Driven Materials Discovery and Design: Engineering Intelligent Solutions for Next-Generation Sustainable Technologies

Abstract

The demand for advanced materials that support sustainable technologies—such as renewable energy systems, green manufacturing, and eco-friendly electronics—has intensified in recent years. However, conventional materials discovery and design processes are often slow, expensive, and heavily reliant on trial-and-error experimentation. This research examines the transformative impact of artificial intelligence (AI) on accelerating materials innovation through predictive modeling, data-driven optimization, and autonomous experimentation. By integrating machine learning with computational simulations and high-throughput screening, researchers can identify novel materials with desired properties in a fraction of the time required by traditional methods. This paper reviews current AI applications in materials informatics, inverse design, and structure-property prediction, while highlighting real-world case studies in battery materials, solar cells, and biodegradable polymers. Furthermore, it addresses the engineering challenges in scaling these AI-driven methods for industrial deployment. The study concludes by outlining future directions for integrating AI into a closed-loop materials innovation ecosystem, ultimately driving the development of intelligent, sustainable, and high-performance technologies.