A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification showcases an innovative use-case of LLMs that extends beyond typical language tasks and into the realm of materials science.

• Model performance: Achieving up to 463% improvement in prediction accuracy over conventional models.
• Prompt engineering synergy: The model utilized a combination of LLM expertise and deep learning.
• Benefit in data-sparse scenarios: Especially useful where conventional datasets are limited.
• Research horizon: Opens new pathways for materials discovery and design innovation.

The use of LLMs to address complex, domain-specific challenges, such as those in materials science, underlines the adaptability and potential of AI. The remarkable performance improvements demonstrated here could herald a new era for computational material science, and inspires further interdisciplinary collaborations.