|
|
|
Adaptive genetic algorithm-based design of gamma-graphyne nanoribbon incorporating diamond-shaped segment with high thermoelectric conversion efficiency |
| Jingyuan Lu(陆静远)1,2,†, Chunfeng Cui(崔春凤)1,2,†, Tao Ouyang(欧阳滔)1,2,‡, Jin Li(李金)1,2, Chaoyu He(何朝宇)1,2, Chao Tang(唐超)1,2,§, and Jianxin Zhong(钟建新)1,2 |
1 School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, China;
2 Hunan Key Laboratory for Micro-Nano Energy Materials and Device, Xiangtan University, Xiangtan 411105, China |
|
|
|
|
Abstract The gamma-graphyne nanoribbons ($\gamma $-GYNRs) incorporating diamond-shaped segment (DSSs) with excellent thermoelectric properties are systematically investigated by combining nonequilibrium Green's functions with adaptive genetic algorithm. Our calculations show that the adaptive genetic algorithm is efficient and accurate in the process of identifying structures with excellent thermoelectric performance. In multiple rounds, an average of 476 candidates (only 2.88% of all 16512 candidate structures) are calculated to obtain the structures with extremely high thermoelectric conversion efficiency. The room temperature thermoelectric figure of merit ($ZT$) of the optimal $\gamma $-GYNR incorporating DSSs is 1.622, which is about 5.4 times higher than that of pristine $\gamma $-GYNR (length 23.693 nm and width 2.660 nm). The significant improvement of thermoelectric performance of the optimal $\gamma $-GYNR is mainly attributed to the maximum balance of inhibition of thermal conductance (proactive effect) and reduction of thermal power factor (side effect). Moreover, through exploration of the main variables affecting the genetic algorithm, it is revealed that the efficiency of the genetic algorithm can be improved by optimizing the initial population gene pool, selecting a higher individual retention rate and a lower mutation rate. The results presented in this paper validate the effectiveness of genetic algorithm in accelerating the exploration of $\gamma $-GYNRs with high thermoelectric conversion efficiency, and could provide a new development solution for carbon-based thermoelectric materials.
|
Received: 06 December 2022
Revised: 04 January 2023
Accepted manuscript online: 31 January 2023
|
|
PACS:
|
84.60.Rb
|
(Thermoelectric, electrogasdynamic and other direct energy conversion)
|
| |
72.15.Jf
|
(Thermoelectric and thermomagnetic effects)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11974300, 11974299, and 12074150), the Natural Science Foundation of Hunan Province, China (Grant No. 2021JJ30645), Scientific Research Fund of Hunan Provincial Education Department (Grant Nos. 20K127, 20A503, and 20B582), Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT13093), the Hunan Provincial Innovation Foundation for Postgraduate (Grant No. CX20220544), Youth Science and Technology Talent Project of Hunan Province, China (Grant No. 2022RC1197). |
Corresponding Authors:
Tao Ouyang, Chao Tang
E-mail: ouyangtao@xtu.edu.cn;tang_chao@xtu.edu.cn
|
Cite this article:
Jingyuan Lu(陆静远), Chunfeng Cui(崔春凤), Tao Ouyang(欧阳滔), Jin Li(李金), Chaoyu He(何朝宇), Chao Tang(唐超), and Jianxin Zhong(钟建新) Adaptive genetic algorithm-based design of gamma-graphyne nanoribbon incorporating diamond-shaped segment with high thermoelectric conversion efficiency 2023 Chin. Phys. B 32 048401
|
[1] Zhu T J, Zhao L D and Fu C U 2020 Annalen Der Physik 532 2000435
[2] He J and Tritt T M 2017 Science 357 6358
[3] Zhang W, Zhang X Q, Liu L, Wang Z Q and Li Z G 2021 Chin. Phys. B 30 77405
[4] Snyder G J and Toberer E S 2008 Nat. Mater. 7 105
[5] Yamamoto T and Watanabe K 2006 Phys. Rev. Lett. 96 255503
[6] Xu Y, Xu X, Zhang W, Ouyang T and Tang C 2019 Acta Phys. Sin. 68 247202 (in Chinese)
[7] Kim W, Zide J, Gossard A, Klenov D, Stemmer S, Shakouri A and Majumdar A 2006 Phys. Rev. Lett. 96 045901
[8] Kim W, Lee W, Lee S M, Kim D and Park J 2022 Nanotechnology 33 175702
[9] Broido and Reinecke 1995 Phys. Rev. B 51 13797
[10] Maleki F, Ovens K, Najafian K, Forghani B, Reinhold C and Forghani R 2020 Neuroimaging Clinics of North America 30 E17
[11] Li Y N, Wu P, Zhang S P, Pei Y L, Yang J G, Chen S and Wang L 2022 Chin. Phys. B 31 47203
[12] Cao Z, Fu Q Q, Gu H, Tian Z, Yaer X, Xing J J, Miao L, Wang X H, Liu H M and Wang J 2021 Chin. Phys. B 30 97204
[13] Seko A, Togo A, Hayashi H, Tsuda K, Chaput L and Tanaka I 2015 Phys. Rev. Lett. 115 205901
[14] Yao T S, Tang C Y, Yang M, Zhu K J, Yan D Y, Yi C J, Feng Z L, Lei H C, Li C H, Wang L, Wang L, Shi Y G, Sun Y J and Ding H 2019 Chin. Phys. Lett. 36 068101
[15] Noé F, Olsson S, Köhler J and Wu H 2019 Science 365 6457
[16] Wei H, Bao H and Ruan X L 2020 Nano Energy 71 104619
[17] Sun H N, Ge Y, Liu W and Liu Z C 2019 Energy 171 37
[18] Che Z G, Chiang T A and Che Z H 2011 International Journal of Innovative Computing Information and Control 7 5839
[19] Djurisi A B, Elazar J M and Raki A D 1997 Appl. Opt. 36 7097
[20] Cao R F, Pei X, Zheng H Q, Hu L Q and Wu Y C 2014 Chinese Medical Journal 127 4152
[21] Katoch S, Chauhan S S and Kumar V 2021 Multimedia Tools and Applications 80 8091
[22] Wang Z L, Adachi Y and Chen Z C 2020 Advanced Theory and Simulations 3 1900197
[23] Xiong R, Sa B, Miao N, Li Y L, Zhou J, Pan Y, Wen C, Wu B and Sun Z 2017 RSC Advances 7 8936
[24] Arabha S and Rajabpour A 2020 Materials Today Commun. 22 100706
[25] Li G X, Li Y L, Liu H B, Guo Y B, Li Y J and Zhu D B 2010 Chem. Commun. 46 3256
[26] Li C, Li J B, Wu F M, Li S S, Xia J B and Wang L W 2011 Journal of Physical Chemistry C 115 23221
[27] Kang J, Li J, Wu F, Li S S and Xia J B 2011 Journal of Physical Chemistry C 115 20466
[28] Pan L D, Zhang L Z, Song B Q, Du S X and Gao H J 2011 Appl. Phys. Lett. 98 173102
[29] Baughman R H, Eckhardt H and Kertesz M 1987 J. Chem. Phys. 87 6687
[30] Zhang Y Y, Pei Q X and Wang C M 2012 Comput. Mater. Sci. 65 406
[31] Liu R, Gao X, Zhou J Y, Xu H, Li Z Z, Zhang S Q, Xie Z Q, Zhang J and Liu Z F 2017 Adv. Mater. 29 1604665
[32] Yang C F, Li Y, Chen Y, Li Q D, Wu L L and Cui X L 2019 Small 15 1804710
[33] Sevincli H and Sevik C 2014 Appl. Phys. Lett. 105 223108
[34] Zhang Y Y, Pei Q X, Hu M and Zong Z 2015 RSC Advances 5 65221
[35] Long M Q, Tang L, Wang D, Li Y L and Shuai Z G 2011 ACS Nano 5 2593
[36] Jiang P H, Liu H J, Cheng L, Fan D D, Zhang J, Wei J, Liang J H and Shi J 2017 Carbon 113 108
[37] Chen B, Li X Q, Xue L, Han Y, Yang Z and Zhang L L 2021 Chin. Phys. B 30 57101
[38] Cranford S W and Buehler M J 2011 Carbon 49 4111
[39] Narita N, Nagai S, Suzuki S and Nakao K 1998 Phys. Rev. B 58 11009
[40] Hou X, Xie Z J, Li C M, Li G N and Chen Z Q 2018 Materials 11 188
[41] Jazzbin E A 2020 http://www.geatpy.com/
[42] Jong D and Kenneth 1980 IEEE Transactions on Systems, Man, and Cybernetics 10 566
[43] Wu S Q, Ji M, Wang C Z, Nguyen M C, Zhao X, Umemoto K, Wentzcovitch R M and Ho K M 2014 J. Phys.: Condens. Matter 26 035402
[44] Zhan L J, Fang Y M, Zhang R T, Lu X C, Lu T Y, Cao X R, Zhu Z Z and Wu S Q 2022 Phys. Chem. Chem. Phys. 24 15201
[45] Ouyang T and Hu M 2014 Nanotechnology 25 245401
[46] Gale J D 2005 Zeitschrift für Kristallographie - Crystalline Materials 220 552 DOI:
[47] Gale and D J 1997 J. Chem. Soc. 93 629 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
