Artificial Intelligence in Materials Discovery: A Comprehensive Review of Methods, Applications, and Future Directions

Abstract

Artificial intelligence (AI) is transforming the landscape of materials discovery by addressing the limitations of traditional experimental and computational approaches. Conventional methods, while foundational, are often slow, resource-intensive, and constrained by the vastness of chemical space. AI techniques—including supervised learning for property prediction, unsupervised learning for pattern recognition, deep learning for complex data analysis, reinforcement learning for adaptive optimization, and generative models for inverse design— offer powerful alternatives that accelerate discovery and innovation. This review provides a comprehensive synthesis of current AI methodologies, their applications in materials science, and the challenges that remain. By bridging materials science and AI, the article highlights how data-driven approaches can enhance reproducibility, efficiency, and scalability in discovery pipelines. Ultimately, the review aims to provide researchers, practitioners, and policymakers with a roadmap for leveraging AI to design advanced materials that address pressing societal needs in energy, healthcare, and sustainability.