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Efficient Discovery of Optimal N-Layered TMDC Hetero-Structures

Published online by Cambridge University Press:  05 March 2018

Lindsay Bassman ,
Pankaj Rajak ,
Rajiv K. Kalia ,
Aiichiro Nakano ,
Fei Sha ,
Muratahan Aykol ,
Patrick Huck ,
Kristin Persson ,
Jifeng Sun  and
David J. Singh
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Lindsay Bassman*
Affiliation:
Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA90089,USA Department of Physics, University of Southern California, Los Angeles, CA90089,USA
Pankaj Rajak
Affiliation:
Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA90089,USA Department of Chemical Engineering and Material Science, University of Southern California, Los Angeles, CA90089,USA
Rajiv K. Kalia
Affiliation:
Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA90089,USA Department of Physics, University of Southern California, Los Angeles, CA90089,USA Department of Computer Science, University of Southern California, Los Angeles, CA90089,USA Department of Chemical Engineering and Material Science, University of Southern California, Los Angeles, CA90089,USA
Aiichiro Nakano
Affiliation:
Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA90089,USA Department of Physics, University of Southern California, Los Angeles, CA90089,USA Department of Computer Science, University of Southern California, Los Angeles, CA90089,USA Department of Chemical Engineering and Material Science, University of Southern California, Los Angeles, CA90089,USA Department of Biological Sciences, University of Southern California, Los Angeles, CA90089,USA
Fei Sha
Affiliation:
Department of Computer Science, University of Southern California, Los Angeles, CA90089,USA Department of Biological Sciences, University of Southern California, Los Angeles, CA90089,USA
Muratahan Aykol
Affiliation:
Lawrence Berkeley National Lab, 1 Cyclotron Rd, Berkeley, CA94720, USA
Patrick Huck
Affiliation:
Lawrence Berkeley National Lab, 1 Cyclotron Rd, Berkeley, CA94720, USA
Kristin Persson
Affiliation:
Lawrence Berkeley National Lab, 1 Cyclotron Rd, Berkeley, CA94720, USA
Jifeng Sun
Affiliation:
Department of Physics and Astronomy, University of Missouri, Columbia, MO65211, USA
David J. Singh
Affiliation:
Department of Physics and Astronomy, University of Missouri, Columbia, MO65211, USA
Priya Vashishta
Affiliation:
Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA90089,USA Department of Physics, University of Southern California, Los Angeles, CA90089,USA Department of Computer Science, University of Southern California, Los Angeles, CA90089,USA Department of Chemical Engineering and Material Science, University of Southern California, Los Angeles, CA90089,USA
*
†(Email: bassman@usc.edu)
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Abstract

Vertical hetero-structures made from stacked monolayers of transition metal dichalcogenides (TMDC) are promising candidates for next-generation optoelectronic and thermoelectric devices. Identification of optimal layered materials for these applications requires the calculation of several physical properties, including electronic band structure and thermal transport coefficients. However, exhaustive screening of the material structure space using ab initio calculations is currently outside the bounds of existing computational resources. Furthermore, the functional form of how the physical properties relate to the structure is unknown, making gradient-based optimization unsuitable. Here, we present a model based on the Bayesian optimization technique to optimize layered TMDC hetero-structures, performing a minimal number of structure calculations. We use the electronic band gap and thermoelectric figure of merit as representative physical properties for optimization. The electronic band structure calculations were performed within the Materials Project framework, while thermoelectric properties were computed with BoltzTraP. With high probability, the Bayesian optimization process is able to discover the optimal hetero-structure after evaluation of only ∼20% of all possible 3-layered structures. In addition, we have used a Gaussian regression model to predict not only the band gap but also the valence band maximum and conduction band minimum energies as a function of the momentum.

Keywords

thermoelectricelectronic structuresimulation
Type
Articles
Information
MRS Advances , Volume 3 , Issue 6-7: Nanomaterials , 2018 , pp. 397 - 402
Copyright
Copyright © Materials Research Society 2018 

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Footnotes

*

These authors contributed equally to this work

References

Gupta, A., Sakthivel, T. and Seal, S., Prog. Mater. Sci. 73, 44–126 (2015).Google Scholar
Venkata Subbaiah, Y., Saji, K. and Tiwari, A., Adv. Funct. Mater. 26(13), 2046–2069 (2016).Google Scholar
Zhang, Y., Tan, Y.-W., Stormer, H. L. and Kim, P., Nature 438 (7065), 201-204 (2005).Google Scholar
Deepak, F., Vinod, C., Mukhopadhyay, K., Govindaraj, A. and Rao, C., Chem. Phys. Lett. 353(5), 345–352 (2002).Google Scholar
Wallace, P. R., Phys. Rev. 71(9), 622 (1947).Google Scholar
Jain, A., Shin, Y. and Persson, K. A., Nat. Rev. Mater. 1, 15004 (2016).Google Scholar
Olivares-Amaya, R., Amador-Bedolla, C., Hachmann, J., Atahan-Evrenk, S., Sanchez-Carrera, R. S., Vogt, L. and Aspuru-Guzik, A., Energy Environ. Sci. 4(12), 4849–4861 (2011).Google Scholar
Rajan, K., Mater. Today 8(10), 38–45 (2005).Google Scholar
LeSar, R., Statistical Analysis and Data Mining: The ASA Data Science Journal 1(6), 372–374 (2009).Google Scholar
Lee, J., Seko, A., Shitara, K., Nakayama, K. and Tanaka, I., Phys. Rev. B 93(11), 115104 (2016).Google Scholar
Gu, T., Lu, W., Bao, X. and Chen, N., Solid State Sci. 8(2), 129–136 (2006).Google Scholar
Kim, C., Pilania, G. and Ramprasad, R., J. Phys. Chem. C 120(27), 14575–14580 (2016).Google Scholar
Kim, C., Pilania, G. and Ramprasad, R., Chem. Mater. 28(5), 1304–1311 (2016).Google Scholar
Zhaochun, Z., Ruiwu, P. and Nianyi, C., Mater. Sci. Eng. B 54(3), 149–152 (1998).Google Scholar
Huan, T. D., Mannodi-Kanakkithodi, A. and Ramprasad, R., Phys. Rev. B 92(1), 014106 (2015).Google Scholar
Forrester, A. I., Sóbester, A. and Keane, A. J., presented at the Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 2007 (unpublished).Google Scholar
Mannodi-Kanakkithodi, A., Pilania, G., Huan, T. D., Lookman, T. and Ramprasad, R., Sci. Rep. 6, 20952 (2016).Google Scholar
Brochu, E., Cora, V. M. and De Freitas, N., arXiv preprint arXiv:1012.2599 (2010).Google Scholar
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. and de Freitas, N., Proc. IEEE 104(1), 148–175 (2016).Google Scholar
Snoek, J., Larochelle, H. and Adams, R. P., presented at the Advances in Neural Information Processing Systems, 2012 (unpublished).Google Scholar
Rasmussen, C. E. and Williams, C. K., Gaussian processes for machine learning. (MIT press Cambridge, 2006).Google Scholar
Kennedy, M. C. and O’Hagan, A., Biometrika 87(1), 1–13 (2000).Google Scholar
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D. and Ceder, G., APL Mater. 1(1), 011002 (2013).Google Scholar
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A. and Ceder, G., Comput. Mater. Sci. 68, 314–319 (2013).Google Scholar
Blöchl, P. E., Phys. Rev. B 50(24), 17953 (1994).Google Scholar
Kresse, G. and Furthmüller, J., Phys. Rev. B 54(16), 11169 (1996).CrossRefGoogle Scholar
Kresse, G. and Furthmüller, J., Comput. Mater. Sci. 6(1), 15–50 (1996).Google Scholar
Perdew, J. P., Burke, K. and Ernzerhof, M., Phys. Rev. Lett. 77(18), 3865 (1996).Google Scholar
Madsen, G. K. and Singh, D. J., Comput. Phys. Commun. 175(1), 67–71 (2006).Google Scholar