Abstract The fact that the properties of thermoelectric materials are to be estimated with Artificial Neural Networks without production and measurement will help researchers in terms of time and cost. For this purpose, figure of merit, which is the performance value of thermoelectric materials, is estimated by Artificial Neural Networks without an experimental study. P-and n-type thermoelectric bulk samples were obtained in 19 different compositions by doping different elements into Ca2.7Ag0.3Co4O9- and Zn0.98Al0.02O-based oxide thermoelectric materials. The Seebeck coefficient, electrical resistivity and thermal diffusivity values of the bulk samples were measured from 200 °C to 800 °C with an increase rate of 100 °C, and figure of merit values were calculated. 7 different Artificial Neural Network models were created using 123 measured results of experimental data and the molar masses of the doping elements. In this system aiming to predict the electrical resistivity, thermal diffusivity and figure of merit values of thermoelectric materials, the average R value and accuracy rate of these values were estimated to be 94% and 80%, respectively.

中文翻译：

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具有创新方法的热电材料开发中人工神经网络的预测模型

摘要 热电材料的性质不用生产和测量就用人工神经网络来估计，这将有助于研究人员在时间和成本方面。为此，热电材料的性能值即品质因数是通过人工神经网络估算的，无需进行实验研究。通过将不同元素掺杂到 Ca2.7Ag0.3Co4O9 和 Zn0.98Al0.02O 基氧化物热电材料中，获得了 19 种不同成分的 P 型和 n 型热电体样品。从 200°C 到 800°C，以 100°C 的增加速率测量大块样品的塞贝克系数、电阻率和热扩散率值，并计算品质因数值。使用 123 个实验数据的测量结果和掺杂元素的摩尔质量创建了 7 个不同的人工神经网络模型。该系统旨在预测热电材料的电阻率、热扩散率和品质因数值，估计这些值的平均 R 值和准确率分别为 94% 和 80%。