npj:深度神经网络模型—快速准确评估三元硫族半导体热电性能
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来自北京航空航天大学材料科学与工程学院的孙志梅教授团队基于第一性原理计算和深度神经网络,仅利用结构和元素周期表相关的特征作为输入参数,发展了两个机器学习模型,来分别快速预测材料在不同温度下的最大ZT值(ZTmax)及其对应的最佳掺杂类型。该研究通过DFT计算与机器学习预测的结果对比,成功验证了模型的准确性。在整个IV-V-VI族中,成功发现了数个具有高ZT值的半导体 (ZTmax>0.8,650 K),特别是 n型Pb2Sb2S5,其在650 K下的ZTmax达到1.2。
另外,结果还表明,Te基半导体的n型热电性能优于其p型,而Se基化合物的p型热电性能优于其n型。该研究通过全面的电子结构和输运性质分析,揭示了Pb2Sb2S5的优异热电性能和Se/Te基掺杂差异性的起源:1)极低热导率和良好的功率因子导致了n型Pb2Sb2S5的高ZT值;2)多导带能谷导致的高n型塞贝克系数和电导率使Te基半导体的n型热电性能优于p型,而高的价带边缘态密度引起的高p型塞贝克系数使得Se基半导体的p型热电性更佳。该工作发现的高性能IV-V-VI热电半导体对将来的实验研究和中温热电应用具有重要意义,且开发的机器学习模型可以合理的应用到其他类型化合物的热电性能预测。
该文近期发表于npj Computational Materials 7: 176 (2020),英文标题与摘要如下,点击左下角“阅读原文”可以自由获取论文PDF。
Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning
Yu Gan, Guanjie Wang, Jian Zhou & Zhimei Sun
Layered IV-V-VI semiconductors have immense potential for thermoelectric (TE) applications due to their intrinsically ultralow lattice thermal conductivity. However, it is extremely difficult to assess their TE performance via experimental trial-and-error methods. Here, we present a machine-learning-based approach to accelerate the discovery of promising thermoelectric candidates in this chalcogenide family. Based on a dataset generated from high-throughput ab initio calculations, we develop two highly accurate-and-efficient neural network models to predict the maximum ZT (ZTmax) and corresponding doping type, respectively. The top candidate, n-type Pb2Sb2S5, is successfully identified, with the ZTmax over 1.0 at 650 K, owing to its ultralow thermal conductivity and decent power factor. Besides, we find that n-type Te-based compounds exhibit a combination of high Seebeck coefficient and electrical conductivity, thereby leading to better TE performance under electron doping than hole doping. Whereas p-type TE performance of Se-based semiconductors is superior to n-type, resulting from large Seebeck coefficient induced by high density-of-states near valence band edges.
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