Accelerating Scientific Discovery with Artificial Intelligence: Applications in Material Science, Drug Discovery, and Climate Modeling

Abstract

Artificial intelligence (AI) has emerged as a transformative tool in scientific research, enabling accelerated discovery and optimization in complex domains. This paper explores the integration of AI techniques including deep learning, generative models, and reinforcement learning—into three critical fields, namely material science, drug discovery, and climate modeling. It discusses the current challenges in each domain, highlight AI-driven methodologies, and propose a framework that leverages data-driven modeling to reduce experimental time, optimize resource allocation, and improve predictive accuracy. Finally, the paper analyzes prospects, emphasizing interpretability, scalability, and ethical considerations in AI-accelerated science.