Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Establishment of a BaTiO3-based Computational Science Platform to Predict Multi-component Properties

다성분계 물성을 예측하기 위한 BaTiO3기반 계산과학 플랫폼 구축

  • Lee, Dong Geon (Center of Materials Digitalization, Korea Institute of Ceramic Engineering and Technology (KICET)) ;
  • Lee, Han Uk (Center of Materials Digitalization, Korea Institute of Ceramic Engineering and Technology (KICET)) ;
  • Im, Won Bin (Division of Materials Science and Engineering, Hanyang University) ;
  • Ko, Hyunseok (Center of Materials Digitalization, Korea Institute of Ceramic Engineering and Technology (KICET)) ;
  • Cho, Sung Beom (Center of Materials Digitalization, Korea Institute of Ceramic Engineering and Technology (KICET))
  • Received : 2022.08.17
  • Accepted : 2022.08.22
  • Published : 2022.09.30
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Abstract

Barium titanate (BaTiO3) is considered to be a beneficial ceramic material for multilayer ceramic capacitor (MLCC) applications because of its high dielectric constant and low dielectric loss. Numerous attempts have been made to improve the physical properties of BaTiO3 in response to recent market trends by employing multicomponent alloying strategies. However, owing to its significant number of atomic combinations and unpredictable physical properties, finding a traditional experimental approach to develop multicomponent systems is difficult; the development of such systems is also time-consuming. In this study, 168 new structures were fabricated using special quasi-random structures (SQSs) of Ba1-xCaxTi1-yZryO3, and 1680 physical properties were extracted from first-principles calculations. In addition, we built an integrated database to manage the computational results, and will provide big data solutions by performing data analysis combined with AI modeling. We believe that our research will enable the global materials market to realize digital transformation through datalization and intelligence of the material development process.

Keywords

Acknowledgement

본 연구는 산업통상자원부의 가상공학플랫폼 구축사업(P0022336)의 지원을 통해 수행되었습니다.

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