Advanced search
Publication Cover
Science and Technology of Advanced Materials Volume 18, 2017 - Issue 1
Submit an article Journal homepage
5,824
Views
48
CrossRef citations to date
0
Altmetric
New topics/Others

MDTS: automatic complex materials design using Monte Carlo tree search

Thaer M. DiebNational Institute for Materials Science, Tsukuba, Japan.;Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.View further author information
,
Shenghong JuDepartment of Mechanical Engineering, The University of Tokyo, Tokyo, Japan.View further author information
,
Kazuki YoshizoeRIKEN, Center for Advanced Intelligence Project, Tokyo, Japan.View further author information
,
Zhufeng HouNational Institute for Materials Science, Tsukuba, Japan.View further author information
,
Junichiro ShiomiNational Institute for Materials Science, Tsukuba, Japan.;Department of Mechanical Engineering, The University of Tokyo, Tokyo, Japan.View further author information
&
Koji TsudaNational Institute for Materials Science, Tsukuba, Japan.;Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.;RIKEN, Center for Advanced Intelligence Project, Tokyo, Japan.Correspondence [email protected]
View further author information
Pages 498-503 | Received 25 Apr 2017, Accepted 15 Jun 2017, Published online: 20 Jul 2017

References

  • Seko A, Togo A, Hayashi H, et al. Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and bayesian optimization. Phys Rev Lett. 2015;115:205901.
  • Balachandran PV, Xue D, Theiler J, et al. Adaptive strategies for materials design using uncertainties. Sci Rep. 2016;6:19660.
  • Okhotnikov K, Charpentier T, Cadars S. Supercell program: a combinatorial structure-generation approach for the local-level modeling of atomic substitutions and partial occupancies in crystals. J Cheminf. 2016;8(1):17. DOI: 10.1186/s13321-016-0129-3
  • Ju S, Shiga T, Feng L, et al. Designing nanostructures for phonon transport via Bayesian optimization. Phys Rev X. 2017;7:021024.
  • Coulinga D, Bernotb R, Dochertyb KM, et al. Assessing the factors responsible for ionic liquid toxicity to aquatic organisms via quantitative structure-property relationship modeling. Green Chem. 2006;8:82–90.
  • Snoek J, Larochelle H, Adams R. Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst. 2012;2951–2959.
  • Ueno T, Rhone T, Hou Z, et al. COMBO: an efficient Bayesian optimization library for materials science. Mater Discov. 2016;4:18–21.
  • Seko A, Maekawa T, Tsuda K, et al. Machine learning with systematic density-functional theory calculations: application to melting temperatures of single-and binary-component solids. Phys Rev B. 2014;89:054303.
  • Kiyohara S, Oda H, Tsuda K, et al. Acceleration of stable interfacestructure searching using a kriging approach. Jpn J Appl Phys. 2016;55:045502.
  • Patra TK, Meenakshisundaram V, Hung J, et al. Neural-network-biased genetic algorithms for materials design: Evolutionary algorithms that learn. ACS Comb Sci. 2017;19(2):96–107.
  • Paszkowicz W, Harris KD, Johnston RL. Genetic algorithms: a universal tool for solving computational tasks in materials science. Comput Mater Sci. 2009;45(1):ix–x.
  • Browne C, Powley E, Whitehouse D, et al. A survey of Monte Carlo tree search methods. IEEE Trans Comput Intell AI in Games. 2012;4(1):1–43.
  • Silver D, Huang A, Maddison C, et al. Mastering the game of Go with deep neural networks and tree search. Nature. 2016;529:484–489.
  • Murakami T, Hori T, Shiga T, et al. Probing and tuning inelastic phonon conductance across finite-thickness interface. Appl Phys Express. 2014;7:121801.
  • Murakami T, Shiga T, Hori T, et al. Importance of local force fields on lattice thermal conductivity reduction in PbTe1–xSex alloys. EPL. 2013;102:46002.
  • Kocsis L, Szepesvári C. Bandit based monte-carlo planning. European conference on machine learning. Berlin: Springer; 2006. p. 282–293.
  • Griebel M, Hamaekers J. Molecular dynamics simulations of the elastic moduli of polymer-carbon nanotube composites. Comput Meth Appl Mech Eng. 2004;193:1773–1788.
  • Griebel M, Knapek S, Zumbusch G. Numerical simulation in molecular dynamics. Vol. 5, Texts in computational science and engineering. Springer-Verlag, Berlin Heidelberg; 2007.
  • Tersoff J. Modeling solid-state chemistry: interatomic potentials for multicomponent systems. Phys Rev B. 1989;39:5566(R).
  • Tersoff J. Erratum: Modeling solid-state chemistry: interatomic potentials for multicomponent systems. Phys Rev B. 1990;41:3248.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.