| Abstract Scope |
The use of Large Language Models (LLMs) in the field of material science presents a transformative approach to the design and discovery of new materials. This talk will explore how LLMs, typically employed for natural language processing tasks, can be adapted to predict material properties, generate novel material compositions, and optimize design processes. By leveraging vast datasets of material properties and combining them with advanced machine learning techniques, LLMs can efficiently navigate the complex landscape of material possibilities. This enables the rapid identification of candidates with desired properties, significantly reducing the time and cost associated with traditional experimental approaches. We will discuss successful case studies, the underlying methodologies, and the potential of LLMs to revolutionize material science, paving the way for innovative applications in various industries, from electronics to sustainable energy solutions. |