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Accelerated Development of Refractory Nanocomposite Solar Absorbers using Bayesian Optimization

Published online by Cambridge University Press:  17 December 2019

Qiangshun Guan ,
Afra S. Alketbi ,
Aikifa Raza  and
TieJun Zhang Open the ORCID record for TieJun Zhang [Opens in a new window]
Qiangshun Guan
Affiliation:
Department of Mechanical Engineering, Masdar Institute, Khalifa University of Science and Technology, P.O. Box 54224, Abu Dhabi, United Arab Emirates. *Correspondence: [email protected]
Afra S. Alketbi
Affiliation:
Department of Mechanical Engineering, Masdar Institute, Khalifa University of Science and Technology, P.O. Box 54224, Abu Dhabi, United Arab Emirates. *Correspondence: [email protected]
Aikifa Raza
Affiliation:
Department of Mechanical Engineering, Masdar Institute, Khalifa University of Science and Technology, P.O. Box 54224, Abu Dhabi, United Arab Emirates. *Correspondence: [email protected]
TieJun Zhang*
Affiliation:
Department of Mechanical Engineering, Masdar Institute, Khalifa University of Science and Technology, P.O. Box 54224, Abu Dhabi, United Arab Emirates. *Correspondence: [email protected]
*
*(Email: [email protected])
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Abstract

Machine learning-based approach is desired for accelerating materials design, development and discovery in combination with high-throughput experiments and simulation. In this work, we propose to apply a Bayesian optimization method to design ultrathin multilayer tungsten-silicon carbide (W-SiC) nanocomposite absorber for high-temperature solar power generation. Based on a semi-analytical scattering matrix method, the design of spectrally selective absorber is optimized over a variety of layer thicknesses to maximize the overall solar absorptance. Our nanofabrication and experimental characterization results demonstrate the capability of the proposed approach for accelerated development of refractory light-absorbing materials. Comparison with other global optimization methods, such as random search, simulated annealing and particle swarm optimization, shows that the Bayesian optimization method can expedite the design of multilayer nanocomposite absorbers and significantly reduce the development cost. This work sheds light on the discovery of novel materials for solar energy and sustainability applications.

Keywords

absorptioncompositenanoscalesputteringthin film
Type
Articles
Information
MRS Advances , Volume 5 , Issue 29-30: Materials Theory, Computation and Characterization , 2020 , pp. 1537 - 1545
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Copyright
Copyright © Materials Research Society 2019

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References

REFERENCES

Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., Montoya, J. H., Dwaraknath, S., Aykol, M., Ortiz, C. and Tribukait, H., Nat. Rev. Mater. 3 (5), 5-20 (2018).10.1038/s41578-018-0005-zCrossRefGoogle Scholar
Frazier, P. I. and Wang, J., in Information Science for Materials Discovery and Design, edited by Lookman, T., Alexander, F. J., and Rajan, K. (Springer Publisher, Switzerland, 2016), p. 45-75.10.1007/978-3-319-23871-5_3CrossRefGoogle Scholar
Snoek, J., Larochelle, H. and Adams, R. P., in Advances in Neural Information Processing Systems 25, edited by Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q. (Curran Associates, Inc. Publisher, 2012), p. 2951-2959.Google Scholar
Seko, A., Maekawa, T., Tsuda, K. and Tanaka, I., Phys. Rev. B Condens. Matter 89 (5), 054303 (2014).10.1103/PhysRevB.89.054303CrossRefGoogle Scholar
Seko, A., Togo, A., Hayashi, H., Tsuda, K., Chaput, L. and Tanaka, I., Phys. Rev. Lett. 115 (20), 205901 (2015).10.1103/PhysRevLett.115.205901CrossRefGoogle Scholar
Ju, S., Shiga, T., Feng, L., Hou, Z., Tsuda, K. and Shiomi, J., Phys. Rev. X 7 (2), 021024 (2017).Google Scholar
Yamawaki, M., Ohnishi, M., Ju, S. and Shiomi, J., Sci. Adv. 4 (6), eaar4192 (2018).10.1126/sciadv.aar4192CrossRefGoogle Scholar
Jalem, R., Kanamori, K., Takeuchi, I., Nakayama, M., Yamasaki, H. and Saito, T., Sci. Rep. 8, 5845 (2018).10.1038/s41598-018-23852-yCrossRefGoogle Scholar
Herbol, H. C., Hu, W., Frazier, P., Clancy, P. and Poloczek, M., Npj Comput. Mater. 4, 51 (2018).10.1038/s41524-018-0106-7CrossRefGoogle Scholar
Lai, F.-D., Li, W.-Y., Chang, K.-C., Wang, Y.-Z., Chi, P.-L. and Su, J.-Y., Integr. Ferroelectr. 137 (1), 77-84 (2012).10.1080/10584587.2012.687276CrossRefGoogle Scholar
Nuru, Z. Y., Arendse, C., Khamlich, S. and Maaza, M., Vacuum 86 (12), 2129-2135 (2012).10.1016/j.vacuum.2012.06.012CrossRefGoogle Scholar
Rebouta, L., Pitães, A., Andritschky, M., Capela, P., Cerqueira, M., Matilainen, A. and Pischow, K., Surf. Coat. Technol. 211, 41-44 (2012).CrossRefGoogle Scholar
An, L., Ali, S. T., Søndergaard, T., Nørgaard, J., Tsao, Y.-C. and Pedersen, K., Solar Energy 118, 410-418 (2015).10.1016/j.solener.2015.05.042CrossRefGoogle Scholar
Cui, S., Weile, D. S. and Volakis, J. L., IEEE Trans. Antennas Propag. 54 (6), 1811-1817 (2006).CrossRefGoogle Scholar
Granier, C. H., Afzal, F. O., Lorenzo, S. G., Reyes, M. Jr, Dowling, J. P. and Veronis, G., J. Appl. Phys. 116, 243101 (2014).CrossRefGoogle Scholar
Sakurai, A., Tanikawa, H. and Yamada, M., J. Quant. Spectrosc. Radiat. Transfer 132, 80-89 (2014).CrossRefGoogle Scholar
Wang, H., Alshehri, H., Su, H. and Wang, L., Sol. Energy Mater. Sol. Cells 174, 445-452 (2018).CrossRefGoogle Scholar
Du, M., Hao, L., Mi, J., Lv, F., Liu, X., Jiang, L. and Wang, S., Sol. Energy Mater. Sol. Cells 95, 1193-1196 (2011).10.1016/j.solmat.2011.01.006CrossRefGoogle Scholar
Wu, Y., Wang, C., Sun, Y., Xue, Y., Ning, Y., Wang, W., Zhao, S., Tomasella, E. and Bousquet, A., Sol. Energy Mater. Sol. Cells 134, 373-380 (2015).10.1016/j.solmat.2014.12.005CrossRefGoogle Scholar
Hernández-Pinilla, D., Rodríguez-Palomo, A., Álvarez-Fraga, L., Céspedes, E., Prieto, J., Muñoz-Martín, A. and Prieto, C., Sol. Energy Mater. Sol. Cells 152, 141-146 (2016).10.1016/j.solmat.2016.04.001CrossRefGoogle Scholar
Kennedy, C. E., National Renewable Energy Lab., Golden, CO.(US) 2002.Google Scholar
Rumpf, R. C., Prog. Electromagn. Res. B 35, 241-261 (2011).10.2528/PIERB11083107CrossRefGoogle Scholar
Redheffer, R., in Modern Mathematics for the Engineer, 2nd ed., edited by Beckenbach, E. F. (Dover Publications, Inc. Publisher, New York, 2013), p. 282-337.Google Scholar
Ueno, T., Rhone, T. D., Hou, Z., Mizoguchi, T. and Tsuda, K., Materials Discovery 4, 18-21 (2016).10.1016/j.md.2016.04.001CrossRefGoogle Scholar
Perry, M., Python Module for Simulated Annealing, Available at:https://github.com/Perrygeo/Simanneal (accessed 1 October 2019).Google Scholar
Chester, D., Bermel, P., Joannopoulos, J. D., Soljacic, M. and Celanovic, I., Opt. Express 19 (103), A245-A257 (2011).10.1364/OE.19.00A245CrossRefGoogle Scholar