Physics-Informed Machine Learning (PIML) refers to a methodology that incorporates physical and chemical principles into machine learning models to enhance their predictive capabilities. By embedding relevant domain knowledge, PIML allows AI to effectively learn from limited data and make accurate predictions about material properties. This approach facilitates the integration of traditional materials informatics (MI) with modern machine learning techniques, driving innovation in material development processes.

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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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