For a long time, design of new materials was implemented on the basis of costly and time-consuming experimental approach. Nowadays machine learning, data mining and Big Data techniques allow to develop a new approach, a so-called data-driven design of new materials. In this paper, we consider an approach to alloy discovery that is based on a synergy of deep learning and fuzzy logic methods. The approach allows to design materials with predefined characteristics by computer-aided generation of the underlying crystal structures and optimization of their parameters. An example is provided to illustrate the approach.


Crystal structure Material properties Fuzzy logic \( \underline{\underline{\text{D}}} {\text{eep}}\; {\text{learning}} \) Big data 


  1. 1.
    Babanli, M.B., Huseynov, V.M.: Z-number-based alloy selection problem. Procedia Comput. Sci. 102, 183–189 (2016)CrossRefGoogle Scholar
  2. 2.
    Babanli, M.B., et al.: Review on the new materials design methods. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds.) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing—ICAFS-2018. Advances in Intelligent Systems and Computing, vol. 896. Springer, Cham (2018)Google Scholar
  3. 3.
    Babanli, M.B.: Synthesis of new materials by using fuzzy and big data concepts. Procedia Comput Sci 120, 104–111 (2017)CrossRefGoogle Scholar
  4. 4.
    Babanli, M.B.: Theory and practice of material development under imperfect information. In: Advances in Intelligent Systems and Computing, vol. 896, pp. 4–14. Springer (2018)Google Scholar
  5. 5.
    Babanli, M.B.: Fuzzy modeling of phase diagram under imprecise thermodynamic data. In: Proceedings of the Tenth World Conference “Intelligent Systems for Industrial Automation”, pp. 265–266. b-Quadrat Verlag (2018)Google Scholar
  6. 6.
    Babanli, M.B.: Fuzzy Logic-Based Material Selection and Synthesis. World Scientific, Singapore (2019)CrossRefGoogle Scholar
  7. 7.
    Babanli, M.B.: Fuzzy logic and fuzzy expert system-based material synthesis methods (2019). IntechOpen, Available from: Scholar
  8. 8.
    Constable, D.J.C.: The practice of chemistry still needs to change. Curr. Opin. Green Sustain. Chem. 7, 60–62 (2017)CrossRefGoogle Scholar
  9. 9.
    Yan, W., Lin, S., Kafka, O.L., et al.: Modeling process-structure-property relationships for additive manufacturing. Front. Mech. Eng. 13, 482 (2018)CrossRefGoogle Scholar
  10. 10.
    Jung, J., Yoon, J.I., Park, H.K., Kim, J.Y., Kim, H.S.: An efficient machine learning approach to establish structure-property linkages. Comput. Mater. Sci. 156, 17–25 (2019)CrossRefGoogle Scholar
  11. 11.
    Su, C., Lv, J., Li, Q., Wang, H., Zhang, L., Wang, Y., Ma, Y.: Construction of crystal structure prototype database: methods and applications. J. Phys.: Condens. Matter 29(16), 165901 (2017)Google Scholar
  12. 12.
    Latypov, M.I., Kalidindi, S.R.: Data-driven reduced order models for effective yield strength and partitioning of strain in multiphase steels. J. Comput. Phys. 346, 242–261 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Yabansu, Y.C., Steinmetz, P., Hötzer, J., Kalidindi, S.R., Nestler, B.: Extraction of reduced-order process-structure linkages from phase-field simulations. Acta Mater. 124, 182–194 (2017)CrossRefGoogle Scholar
  14. 14.
    Paulson, N.H., Priddy, M.W., McDowell, D.L., Kalidindi, S.R.: Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics. Acta Mater. 129, 428–438 (2017)CrossRefGoogle Scholar
  15. 15.
    Khosravani, A., Cecen, A., Kalidindi, S.R.: Development of high throughput assays for establishing process-structure-property linkages in multiphase polycrystalline metals: application to dual-phase steels. Acta Mater. 123, 55–69 (2017)CrossRefGoogle Scholar
  16. 16.
    Iskakov, A., Yabansu, Y.C., Rajagopalan, S., Kapustina, A., Kalidindi, S.R.: Application of spherical indentation and the materials knowledge system framework to establishing microstructure-yield strength linkages from carbon steel scoops excised from high-temperature exposed components. Acta Mater. 144, 758–767 (2017)CrossRefGoogle Scholar
  17. 17.
    Yabansu, Y.C., Patel, D.K., Kalidindi, S.R.: Calibrated localization relationships for elastic response of polycrystalline aggregates. Acta Mater. 81, 151–160 (2014)CrossRefGoogle Scholar
  18. 18.
    Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., Kim, C.: Machine learning in materials informatics: recent applications and prospects. npj Comput. Mater. 3, 54 (2017)CrossRefGoogle Scholar
  19. 19.
    Seko, A., Togo, A., Tanaka, I.: Descriptors for machine learning of materials data. In: Tanaka, I. (ed.) Nanoinformatics. Springer, Singapore (2018)Google Scholar
  20. 20.
    Kim, E., Huang, K., Saunders, A., McCallum, A., Ceder, G., Olivetti, E.: Materials synthesis insights from scientific literature via text extraction and machine learning. Chem. Mater. 29(21), 9436–9444 (2017)CrossRefGoogle Scholar
  21. 21.
    Kim, E., Huang, K., Jegelka, S., Olivetti, E.: Virtual screening of inorganic materials synthesis parameters with deep learning. npj Comput. Mater. 3, Article no. 53 (2017)Google Scholar
  22. 22.
    Gubaev, K., Podryabinkin, E.V., Hart, G.L., Shapeev, A.V.: Accelerating high-throughput searches for new alloys with active learning of interatomic potentials. Comput. Mater. Sci. 156, 148–156 (2019)CrossRefGoogle Scholar
  23. 23.
    Han, Y., Yang, X., Zeng, W., Lu, W.: Nonlinear relationship between processing parameters and mechanical properties in Ti6Al4V alloy by using fuzzy neural network. In: Venkatesh, V., Pilchak, A.L., Allison, J.E., Ankem, S., Boyer, R., Christodoulou, J., Fraser, H.L., Imam, M.A., Kosaka, Y., Rack, H.J., Chatterjee, A., Woodfield, A. (eds.) Proceedings of the 13th World Conference on Titanium (2016)Google Scholar
  24. 24.
    Marwin, H.S.S., Preuss, M., Waller, M.P.: Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555, 604–610 (2018)CrossRefGoogle Scholar
  25. 25.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  26. 26.
    Yann, L., Yoshua, B., Geoffrey, H.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  27. 27.
    Prokoshkin, S.D., Khmelevskaya, I.Yu., Korotitskij, A.V., Trubitsyna, I.B., Brailovskij, V., Tyurenn, S.: On the lattice parameters of the B19’ martensite in binary Ti-Ni shape memory alloys. Fiz. Met. Metalloved. 96(1), 62–71 (2003)Google Scholar
  28. 28.
    Aliev, R.A., Perdycz, W., Huseynov, O.H.: Functions defined on a set of Z-numbers. Inf. Sci. 423, 353–375 (2018)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Aliev, R.A., Alizadeh, A.V., Huseynov, O.H., Jabbarova, K.I.: Z-number-based linear programming. Int. J. Intell. Syst. 30(5), 563–589 (2015)CrossRefGoogle Scholar
  30. 30.
    Aliev, R.A., Pedrycz, W.: Fundamentals of a fuzzy-logic-based generalized theory of stability. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(4), 971–988 (2009)CrossRefGoogle Scholar
  31. 31.
    Aliev, R.A., Alizadeh, A.V., Huseynov, O.H.: The arithmetic of continuous Z-numbers. Inf. Sci. 373, 441–460 (2016)CrossRefGoogle Scholar
  32. 32.
    Aliev, R., Tserkovny, A.: Systemic approach to fuzzy logic formalization for approximate reasoning. Inf. Sci. 181(6), 1045–1059 (2011)MathSciNetCrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Azerbaijan State University of Oil and IndustryBakuAzerbaijan

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