Materials Informatics (MI) refers to the application of data-driven techniques, including artificial intelligence and machine learning, to accelerate material development. MI involves integrating vast amounts of literature, experimental data, and domain knowledge (e.g., physical and chemical principles) to predict material properties and optimize experimental designs. By leveraging MI, the platform aims to streamline the material discovery process and enhance innovation in the field through data analysis and predictive modeling.

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LLMs and MI Bring Innovation to Material Development Platforms

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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.

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