AI-driven literature mining speeds discovery of heat-stable lead-free dielectric materials

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 ...

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.

Conceptual image illustrating how a machine-learning model trained on data constructed through multimodal literature mining and physical knowledge explores the vast compositional space of lead-free dielectrics and identifies candidate materials for experimental validation. Credit: Seoul National University College of Engineering Read more

Temperature-dependent performance of the lead-free dielectrics (left) 'SNBTS1,' containing 1 mol% tin (Sn), and (right) 'SNBTS2,' containing 2 mol% Sn. The colored solid lines represent experimental measurements, while the black dashed lines indicate machine-learning predictions. Both materials maintained high dielectric constants—their ability to store electrical energy—over a broad temperature range, while the predicted results closely reproduced the trends observed experimentally. The lower curves indicate the degree of electrical energy loss. Credit: Nature Communications Read more

(Left) Performance comparison between lead-free dielectrics previously reported in the literature and the materials developed in this study. The stars indicate the reference material without tin (SNBT) and the materials containing 1 and 2 mol% Sn (SNBTS1 and SNBTS2), respectively. Materials positioned farther toward the upper right exhibit both higher dielectric constants and better temperature stability. (Right) Comparison of the temperature-dependent dielectric constants of the materials developed in this study with those of barium titanate (BaTiO₃), a representative dielectric material. While the dielectric constant of barium titanate changes sharply at around 125°C, SNBTS1 and SNBTS2 maintain relatively stable dielectric constants over a broad temperature range. Credit: Nature Communications Read more