Skip to main content Accessibility help

Efficient Discovery of Optimal N-Layered TMDC Hetero-Structures

  • Lindsay Bassman (a1) (a2), Pankaj Rajak (a1) (a3), Rajiv K. Kalia (a1) (a2) (a4) (a3), Aiichiro Nakano (a1) (a2) (a4) (a3) (a5), Fei Sha (a4) (a5), Muratahan Aykol (a6), Patrick Huck (a6), Kristin Persson (a6), Jifeng Sun (a7), David J. Singh (a7) and Priya Vashishta (a1) (a2) (a4) (a3)...


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.


Corresponding author



Hide All

These authors contributed equally to this work



Hide All
1.Gupta, A., Sakthivel, T. and Seal, S., Prog. Mater. Sci. 73, 44126 (2015).
2.Venkata Subbaiah, Y., Saji, K. and Tiwari, A., Adv. Funct. Mater. 26(13), 20462069 (2016).
3.Zhang, Y., Tan, Y.-W., Stormer, H. L. and Kim, P., Nature 438 (7065), 201-204 (2005).
4.Deepak, F., Vinod, C., Mukhopadhyay, K., Govindaraj, A. and Rao, C., Chem. Phys. Lett. 353(5), 345352 (2002).
5.Wallace, P. R., Phys. Rev. 71(9), 622 (1947).
6.Jain, A., Shin, Y. and Persson, K. A., Nat. Rev. Mater. 1, 15004 (2016).
7.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), 48494861 (2011).
8.Rajan, K., Mater. Today 8(10), 3845 (2005).
9.LeSar, R., Statistical Analysis and Data Mining: The ASA Data Science Journal 1(6), 372374 (2009).
10.Lee, J., Seko, A., Shitara, K., Nakayama, K. and Tanaka, I., Phys. Rev. B 93(11), 115104 (2016).
11.Gu, T., Lu, W., Bao, X. and Chen, N., Solid State Sci. 8(2), 129136 (2006).
12.Kim, C., Pilania, G. and Ramprasad, R., J. Phys. Chem. C 120(27), 1457514580 (2016).
13.Kim, C., Pilania, G. and Ramprasad, R., Chem. Mater. 28(5), 13041311 (2016).
14.Zhaochun, Z., Ruiwu, P. and Nianyi, C., Mater. Sci. Eng. B 54(3), 149152 (1998).
15.Huan, T. D., Mannodi-Kanakkithodi, A. and Ramprasad, R., Phys. Rev. B 92(1), 014106 (2015).
16.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).
17.Mannodi-Kanakkithodi, A., Pilania, G., Huan, T. D., Lookman, T. and Ramprasad, R., Sci. Rep. 6, 20952 (2016).
18.Brochu, E., Cora, V. M. and De Freitas, N., arXiv preprint arXiv:1012.2599 (2010).
19.Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. and de Freitas, N., Proc. IEEE 104(1), 148175 (2016).
20.Snoek, J., Larochelle, H. and Adams, R. P., presented at the Advances in Neural Information Processing Systems, 2012 (unpublished).
21.Rasmussen, C. E. and Williams, C. K., Gaussian processes for machine learning. (MIT press Cambridge, 2006).
22.Kennedy, M. C. and O’Hagan, A., Biometrika 87(1), 113 (2000).
23.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).
24.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, 314319 (2013).
25.Blöchl, P. E., Phys. Rev. B 50(24), 17953 (1994).
26.Kresse, G. and Furthmüller, J., Phys. Rev. B 54(16), 11169 (1996).
27.Kresse, G. and Furthmüller, J., Comput. Mater. Sci. 6(1), 1550 (1996).
28.Perdew, J. P., Burke, K. and Ernzerhof, M., Phys. Rev. Lett. 77(18), 3865 (1996).
29.Madsen, G. K. and Singh, D. J., Comput. Phys. Commun. 175(1), 6771 (2006).



Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed