Predicting material properties remains a major challenge in materials science, as it often requires complex and computationally intensive calculations. In particular, understanding how materials respond to electric fields is essential for the development of next-generation electronic devices.
To address this challenge, a research group at Tohoku University, led by graduate student Atsushi Takigawa (Graduate School of Engineering), in collaboration with Lecturer Shin Kiyohara and Professor Yu Kumagai, has developed a new AI-based method that enables the rapid screening of thousands of materials, accelerating the identification of promising material candidates. The findings are published in the journal Physical Review X.
A key feature of this approach is the integration of AI with physics-based modeling, resulting in significantly higher accuracy than conventional methods. Rather than directly predicting complex properties such as the dielectric constant, the model first evaluates more basic ones. For example, these include Born effective charges, which describe how atoms respond to electric fields, and phonon properties, which capture atomic vibrations within a material. The model then combines these components to reconstruct the overall material property.
"By teaching AI the underlying physics and letting it uncover how the material behaves, we can make predictions that are not only faster but also more reliable, thereby enabling the rapid screening of superior materials," said Takigawa.
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