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Monte Carlo tree search for materials design and discovery

  • Thaer M. Dieb (a1) (a2) (a3), Shenghong Ju (a1) (a4), Junichiro Shiomi (a1) (a4) (a5) and Koji Tsuda (a1) (a2) (a3)


Materials design and discovery can be represented as selecting the optimal structure from a space of candidates that optimizes a target property. Since the number of candidates can be exponentially proportional to the structure determination variables, the optimal structure must be obtained efficiently. Recently, inspired by its success in the Go computer game, several approaches have applied Monte Carlo tree search (MCTS) to solve optimization problems in natural sciences including materials science. In this paper, we briefly reviewed applications of MCTS in materials design and discovery, and analyzed its future potential.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Corresponding author

Address all correspondence to Koji Tsuda at


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1.Sinnott, S.B.: Material design and discovery with computational materials science. J. Vac. Sci. Technol. A 31, 050812 (2013).
2.Seko, A., Togo, A., Hayashi, H., Tsuda, K., Chaput, L., and Tanaka, I.: Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization. Phys. Rev. Lett. 115, 205901 (2015).
3.Balachandran, P.V., Xue, D., Theiler, J., Hogden, J., and Lookman, T.: Adaptive strategies for materials design using uncertainties. Sci. Rep. 6, 19660 (2016).
4.Okhotnikov, K., Charpentier, T., and Cadars, S.: Supercell program: a combinatorial structure-generation approach for the local-level modeling of atomic substitutions and partial occupancies in crystals. J. Cheminf. 8, 17 (2016).
5.Ju, S., Shiga, T., Feng, L., Hou, Z., Tsuda, K., and Shiomi, J.: Designing nanostructures for phonon transport via Bayesian optimization. Phys. Rev. X 7, 021024 (2017).
6.Agrawal, A. and Choudhary, A.: Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science. APL Mater. 4, 053208 (2016).
7.Drosback, M., Materials Genome Initiative: Advances and Initiatives, JOM, 66, 334335, (2014).
8.Dieb, T.M. and Tsuda, K.: Machine learning-based experimental design in materials science. In Nanoinformatics, edited by Tanaka, I. (Springer, Singapore, 2018). pp. 6574.
9.Patra, T.K., Meenakshisundaram, V., Hung, J., and Simmons, D.: Neural-network-biased genetic algorithms for materials design: Evolutionary algorithms that learn. ACS Comb. Sci. 19, 96 (2017).
10.Paszkowicz, W., Harris, K.D., and Johnston, R.L.: Genetic algorithms: A universal tool for solving computational tasks in Materials Science. Comput. Mater. Sci. 45, ix (2009).
11.Snoek, J., Larochelle, H., and Adams, R.: Practical Bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst. 25, 29512959 (2012).
12.Jones, D.R., Schonlau, M., and Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13, 455 (1998).
13.Ueno, T., Rhone, T., Hou, Z., Mizoguchi, T., and Tsuda, K.: COMBO: an efficient Bayesian optimization library for materials science. Mater. Discov. 4, 1821 (2016).
14.Kiyohara, S., Oda, H., Tsuda, K., and Mizoguchi, T.: Acceleration of stable interface structure searching using a kriging approach. Jpn. J. Appl. Phys. 55, 045502 (2016).
15.Aggarwal, R., Demkowicz, M.J., and Marzouk, Y.M.: Bayesian inference of substrate properties from film behavior. Modell. Simul. Mater. Sci. Eng. 23, 015009 (2015).
16.Browne, C., Powley, E., Whitehouse, D., Lucas, S.M., Cowling, P.I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., and Colton, S.: A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4, 143 (2012).
17.Silver, D., Huang, A., Maddison, C., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D.: Mastering the game of Go with deep neural networks and tree search. Nature 529, 484 (2016).
18.Mehat, J., and Cazenave, T.: Combining UCT and nested Monte Carlo search for single-player general game playing. IEEE Trans. Comp. Intell. AI Games 2, 271 (2010).
19.Yang, X., Zhang, J., Yoshizoe, K., Terayama, K., and Tsuda, K.: ChemTS: an efficient python library for de novo molecular generation. Sci. Technol. Adv. Mater. 18, 972 (2017).
20.Segler, M.H.S., Preuss, M., and Waller, M. P.: Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555(7698), 604610 (2018).
21.Dieb, T.M., Ju, S., Yoshizoe, K., Hou, Z., Shiomi, J., and Tsuda, K.: MDTS: automatic complex materials design using Monte Carlo tree search. Sci. Technol. Adv. Mater. 18, 498 (2017).
22.Kocsis, L. and Szepesvári, C.: Bandit based Monte-Carlo Planning in Machine Learning: ECML 2006 (Springer, Berlin, Heidelberg, 2006) pp. 282293.
23.Kiyohara, S. and Mizoguchi, T.: Searching the stable segregation configuration at the grain boundary by a Monte Carlo tree search. J. Chem. Phys. 148, 241741 (2018).
24.Kiyohara, S. and Mizoguchi, T.: Investigation of segregation of silver at copper grain boundaries by first principles and empirical potential calculations. AIP Conf. Proc. 1763, 040001 (2016).
25.Cao, Z., Zhao, Y., Liao, J., and Yang, X.: Gap maximum of graphene nanoflakes: a first principles study combined with the Monte Carlo tree search method. RSC Adv. 7, 37881 (2017).
26.Ju, S., Dieb, T.M., Tsuda, K., and Shiomi, J.: Optimizing Interface/Surface Roughness for Thermal Transport. Machine Learning for Molecules and Materials NIPS 2018 Workshop (2018).
27.Zhang, W., Fisher, T. S., and Mingo, N.: Simulation of interfacial phonon transport in Si–Ge heterostructures using an atomistic Green's function method. J. Heat Transfer 129, 483491, (2006).
28.Wang, J., Wang, J., and Zeng, N.: Nonequilibrium Green's function approach to mesoscopic thermal transport. Phys. Rev. B 74, 033408, (2006).
29.Dieb, T.M., Hou, Z., and Tsuda, K.: Structure prediction of boron-doped graphene by machine learning. J. Chem. Phys. 148, 241716 (2018).
30.Rasmussen, C.E. and Williams, C.K.I., eds.: Gaussian Processes for Machine Learning (MIT Press, Cambridge, MA, 2006).
31.Kresse, G., and Furthmuller, J.: Efficiency of ab-initio total energy calculations for metals an semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6, 15 (1996).


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