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AI-Powered Tools Revolutionize Materials Science Data Collection

NIMS researchers use ChatGPT to automate data extraction from scientific papers, accelerating materials property database construction for faster innovation in technology and materials science.

This news matters because it addresses a fundamental bottleneck in materials science: the slow, manual process of collecting experimental data from scientific papers. By automating data extraction with large language models, researchers can build comprehensive databases faster, unlocking insights from millions of untapped papers. This accelerates the development of new materials for technologies like smartphones, electric vehicles, and renewable energy systems, potentially leading to breakthroughs in efficiency, sustainability, and performance. For society, it means quicker innovation in everyday products and industrial applications, reducing reliance on trial-and-error methods and fostering a more data-driven approach to scientific discovery. Ultimately, this could lower costs, improve product quality, and address global challenges like climate change through advanced material solutions.