| Abstract Scope |
The integration of large language models (LLMs) into materials science and manufacturing offers unprecedented opportunities for accelerating innovation and enhancing efficiency. We demonstrate how LLMs can automatically extract and synthesize valuable information from vast unstructured texts, including experimental procedures, material properties, and synthesis conditions, thereby building comprehensive datasets for machine learning applications. In materials discovery, we leverage LLMs to generate hypotheses for novel compounds by predicting plausible chemical structures and estimating their properties using natural language descriptions. For manufacturing processes, LLMs analyze technical manuals, operational logs, and maintenance records to optimize process parameters, improve predictive maintenance, and reduce downtime. Case studies highlight the successful identification of new high-temperature superconductors and the optimization of additive manufacturing settings for aerospace-grade alloys. Our results underscore the potential of LLMs to revolutionize materials research and industrial manufacturing by providing sophisticated tools for data-driven decision-making and innovation acceleration. |