Revolutionizing Computational Material Science with ChatGPT: A Framework for AI-Driven Discoveries

Computational material science has become a central technique to address global challenges in energy storage, sustainable materials, and advanced electronics, yet traditional workflows are constrained by high computational demands and multidisciplinary complexity. This research presents, as the first of its kind, a pioneering framework for the integration of ChatGPT, an advanced conversational AI, into computational material science workflows. The proposed framework automates data synthesis, simulation parameter optimization, and hypothesis generation, yet critical bottlenecks in the research process.Efficiency gains on case studies are demonstrated with reductions of 75% in data synthesis timing and 15% in simulation accuracy. ChatGPT’s predictive ability at these scales underscores its ability to simplify experimental design, improve material property predictions, and optimize simulations. There, however, are still numerous challenges: ethical, contextual, and computational.In conclusion, this study exemplifies how ChatGPT serves as a transformer in cranking up material discovery processes, providing a route to a scalable, flexible, collective approach to research. This framework closes the loop between material science and the AI tools we use by bridging the gap between what machines provide and the expertise of human researchers.