This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Artificial intelligence has analyzed data scattered across hundreds of research papers to discover new lead-free dielectric materials that maintain stable performance even at high temperatures. The study presents a new approach that could transform materials discovery from a trial-and-error process into a data-driven one.
Seoul National University College of Engineering announced that a research team led by professor Ho Won Jang of the Department of Materials Science and Engineering has developed a technology for designing lead-free dielectric materials by combining data extracted from scientific literature with physics-informed machine learning. Kwanwoo Song, an integrated M.S./Ph.D. student, served as the first author and led the overall research, while integrated M.S./Ph.D. student Youngmin Kim and postdoctoral researcher Jaehyun Kim participated in the collaborative study. Dielectrics are insulating materials that prevent electricity from flowing directly while storing electric charge, and they are key materials in multilayer ceramic capacitors (MLCCs) used in smartphones, electric vehicles and other electronic devices.
The higher the dielectric constant, the more electrical energy a component of the same size can store. For practical use in electronic devices, however, dielectric performance must also remain stable at high temperatures. The research team combined multimodal literature mining, which automatically extracts information distributed across the text, tables and graphs of scientific papers, with physics-informed machine learning to develop an inverse-design approach that first identifies compositions with a high likelihood of meeting targeted performance requirements.
After constructing a dataset of 1,202 dielectric-property records from 448 papers, the researchers explored a virtual compositional space of approximately 150 million possibilities and narrowed it to 37 candidates. They then synthesized two of these compositions and experimentally confirmed both high dielectric constants and excellent high-temperature stability. The findings are published in the journal Nature Communications.
As more electronic devices operate at high temperatures—including electric vehicles, power electronics and aerospace equipment—the importance of dielectric materials that maintain stable performance despite temperature fluctuations is increasing. In particular, relaxor ferroelectrics, whose electrical response changes relatively gradually with temperature, have the potential to combine high dielectric constants with stability across a broad temperature range. Yet even when the search is restricted to lead-free compositions, the number of potential candidates is virtually limitless because of the many possible combinations of elements and mixing ratios, making trial-and-error exploration costly and time-consuming.
Moreover, relevant data are scattered across the text, tables and graphs of different papers, while measurement conditions such as temperature, frequency and sample characteristics vary from study to study, making the data difficult to use directly for machine-learning training. To address these challenges, the researchers developed a machine-learning framework that integrates information distributed across multiple papers into a unified format while incorporating physical laws to screen for materials that can realistically exist. The team used large language models to organize composition and processing conditions from the text and tables of research papers, while converting graphs into numerical data to extract temperature-dependent dielectric properties.
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