首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods
【24h】

Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods

机译:基于引力优化支持向量回归和极限学习机计算方法的钛酸钡半导体带隙表征

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Barium titanate (BaTiO3) is a class of ceramic multifunctional materials with unique thermal stability, prominent piezoelectricity constant, excellent dielectric constant, environmental friendliness, and excellent photocatalytic activities. These features have rendered barium titanate indispensable in many areas of applications such as electromechanical devices, thermistors, multilayer capacitors, and electrooptical devices. The photocatalytic activity of barium titanate semiconductor is hindered by its large band gap and high rate of charge recombination. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. This present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine- (ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. The developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036ev, 1.145ev, and 0.122ev, respectively. The developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. The developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. The demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources.
机译:钛酸钡(BaTiO3)是一类陶瓷多功能材料,具有独特的热稳定性、突出的压电常数、优异的介电常数、环境友好性和优异的光催化活性。这些特性使钛酸钡在机电器件、热敏电阻、多层电容器和电光器件等许多应用领域中不可或缺。钛酸钡半导体的光催化活性受到其大带隙和高电荷复合率的阻碍。掺杂母体钛酸钡化合物用于带隙调谐具有挑战性,并且会消耗大量时间和其他宝贵资源。本工作通过开发基于极端学习机(ELM-)的模型,以及使用掺杂产生的结构晶格畸变作为模型描述符,将支持向量回归(SVR)与引力搜索算法(GSA)的杂交,将异物掺入母体钛酸钡中的影响与相应的能带隙联系起来。所开发的重力优化SVR(GSVR)的平均绝对误差(MAE)、平均绝对百分比误差(MAPE)和均方根误差(RMSE)分别为0.036ev、1.145ev和0.122ev。开发的 GSVR 模型使用各种性能评估器优于 ELM-Sine 和 ELM-Sig 模型。所建立的GSVR模型研究了碘和钐掺入对母体钛酸钡带隙的意义,所获得的能隙与实验报告的值非常吻合。从其估计值与测量值的接近程度来看,所开发的GSVR所证明的精度提供了一种快速准确的能隙表征方法,避免了实验应力并节省了宝贵的时间和其他资源。

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号