Authors: Yuting He | Wenbo Yan | Yongbin Liu | Yan Wang | Zhixin He | Ruifeng Yao | Jinghui Gao | Lisheng Zhong | Qinxue Yu
Source:  (article, 2018-05-01)
Field: Materials Science — Materials Chemistry

Searching high dielectric permittivity in barium titanate based material by machine learning prediction As the increasing requirement of compact and light electronic and electrical system, especially for high energy density storage system, the development of high dielectric permittivity materials becomes one of critical solutions. Among all materials, barium titanate materials have been found to be a promising ceramic capacitor dielectric system, like BaTi <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-x</sub> Hf <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> material and BaTi <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-x</sub> Sn <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> material. However, to search the optimal composition, mass samples are needed by the traditional method of exhaustion, which increases the cost of work. In order to access the optimal composition of high permittivity efficiently, a machine learning prediction is employed to the searching process in Sn/Ca doped barium titanate ceramics. The machine learning prediction is iteration between theoretical predictions and experiment data. According to our prediction, the searching result shows Ba <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.86</sub> Ca <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.14</sub> Ti <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.86</sub> Sn <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.14</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> has the peak values of dielectric permittivity with ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> =2.0×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> at T=13°C. As a result, sample preparation number reduces to 5.5% compared with the traditional method of exhaustion. In addition, good temperature stability has been found in Ba <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.86</sub> Ca <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.14</sub> Ti <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.86</sub> Sn <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.14</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> because of the relaxor behavior. Barium titanate ; Dielectric ; Artificial intelligence ; Dielectric permittivity ; Ceramic ; Algorithm ; Permittivity ; Capacitor ; Machine learning ; Computer science ; Materials science ; Physics ; Optoelectronics ; Composite material Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Dielectric materials and actuators