Information Extraction Technology refers to AI-powered techniques used to automatically identify, extract, and structure relevant data from unstructured sources like scientific papers, patents, and experimental results in the field of material development. This technology helps the platform gather key information such as material properties, experimental methods, and results, making it easier to organize and present the data for further analysis and decision-making.

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LLMs and MI Bring Innovation to Material Development Platforms

In this paper, we introduce efforts to apply large language models (LLMs) to the field of material development. NEC is advancing the development of a material development platform. By applying core technologies corresponding to two material development steps, namely investigation activities (Read paper/patent) and experimental planning (Design Experiment Plan), the platform organizes documents such as papers and reports as well as data such as experimental results and then presents in an interactive way to users. In addition, with techniquesthat reflect physical and chemical principles into machine learning models, AI can learn even with limited data and accurately predict material properties. Through this platform, we aim to achieve the seamless integration of materials informatics (MI) with a vast body of industry literature and knowledge, thereby bringing innovation to the material development process.