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Reviewing the Novel Machine Learning Tools for Materials Design

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Recent Advances in Technology Research and Education (INTER-ACADEMIA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 660))

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Abstract

Computational materials design is a rapidly evolving field of challenges and opportunities aiming at development and application of multi-scale methods to simulate, predict and select innovative materials with high accuracy. Today the latest advancements in machine learning, deep learning, internet of things (IoT), big data, and intelligent optimization have highly revolutionized the computational methodologies used for materials design innovation. Such novelties in computation enable the development of problem-specific solvers with vast potential applications in industry and business. This paper reviews the state of the art of technological advancements that machine learning tools, in particular, have brought for materials design innovation. Further via presenting a case study the potential of such novel computational tools are discussed for the virtual design and simulation of innovative materials in modeling the fundamental properties and behavior of a wide range of multi-scale materials design problems.

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References

  1. Artrith, N., Urban, A.: An implementation of artificial neural-network potentials for atomistic materials simulations. Comput. Mater. Sci. 114, 135–150 (2016)

    Article  Google Scholar 

  2. Bayer, F.: Robust economic model predictive control using stochastic information. Autom. 74, 151–161 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  3. Battiti, R., Brunato, M.: The LION Way: Machine Learning, Lionlab (2015)

    Google Scholar 

  4. Brunato, M., Battiti, R.: Learning and intelligent optimization: one ring to rule them all. Proc. VLDB Endow. 6, 1176–1177 (2013)

    Article  Google Scholar 

  5. Mosavi, A: Predictive decision model (2015). https://doi.org/10.13140/RG.2.2.21094.63047

  6. Ceder, G.: Challenges for materials design. Mater. Res. 35, 693–701 (2010)

    Google Scholar 

  7. Fischer, C.: Predicting crystal structure by merging data mining. Nature 5, 641–646 (2006)

    Article  Google Scholar 

  8. Jain, A.: A high-throughput infrastructure for density functional theory calculations. Comp. Mater. Sci. 50, 2295–2310 (2011)

    Article  Google Scholar 

  9. Nanthakumar, S., Zhuang, X., Park, H., Rabczuk, T.: Topology optimization of flexoelectric structures. J. Mech. Phys. Solids 105, 217–234 (2017)

    Article  MathSciNet  Google Scholar 

  10. Lencer, D.: A map for phase-change materials. Nat. Mater. 7, 972–977 (2008)

    Article  Google Scholar 

  11. Mosavi, A.: Decision-making software architecture; the visualization and data mining assisted approach. Inf. Comput. Sci. 3, 12–26 (2014)

    Google Scholar 

  12. Milani, A.: Multiple criteria decision making with life cycle assessment for material selection of composites. Express Polym. Lett. 5, 1062–1074 (2011)

    Article  Google Scholar 

  13. Mosavi, A., Vaezipour, A.: Reactive search optimization; application to multiobjective optimization problems. Appl. Math. 3, 1572–1582 (2012)

    Article  Google Scholar 

  14. Mosavi, A.: A multicriteria decision making environment for engineering design and production decision-making. Int. J. Comput. Appl. 69, 26–38 (2013)

    Google Scholar 

  15. Mosavi, A.: Decision-making in complicated geometrical problems. Int. J. Comput. Appl. 87, 22–25 (2014)

    Google Scholar 

  16. Mosavi, A., Varkonyi, A.: Learning in robotics. Int. J. Comput. Appl. 157, 8–11 (2017)

    Google Scholar 

  17. Mcdowell, D.: Simulation-assisted design of materials. Microstruct. 65, 617–647 (2010)

    Google Scholar 

  18. Mosavi, A., et al.: Multiple criteria decision making integrated with mechanical modeling of draping for material selection of textile composites. In: Composite Materials 18 (2012)

    Google Scholar 

  19. Saito, T.: Computational materials design, vol. 34. Springer Science & Business Media, Heidelberg (2013)

    Google Scholar 

  20. Stucke, D.: Predictions of new crystalline states. Nano Lett. 3, 1183–1186 (2003)

    Article  Google Scholar 

  21. Mosavi, A.: Optimal engineering design. Technical report, University of Debrecen (2013)

    Google Scholar 

  22. Curtarolo, S.: High-throughput computational materials design. Nature 12, 191–201 (2013)

    Article  Google Scholar 

  23. Setyawan, W., Curtarolo, S.: High-throughput electronic band structure calculations: challenges and tools. Comp. Mater. Sci. 49, 299–312 (2010)

    Article  Google Scholar 

  24. Kolmogorov, A.: Prediction of new crystal structure phases. Phys. Rev. 73, 180–195 (2006)

    Article  Google Scholar 

  25. Rajan, K.: Materials informatics. Mater. Today 8, 38–45 (2005)

    Article  Google Scholar 

  26. Levy, O.: Uncovering compounds by synergy of cluster expansion and high-throughput methods. J. Am. Chem. Soc. 132, 4830–4833 (2010)

    Article  Google Scholar 

  27. Bhadeshia, H.: Neural networks in materials science. Mater. Sci. 25, 504–510 (2009)

    MathSciNet  Google Scholar 

  28. Mosavi, A.: Computational modeling of the multi-field problems in engineering: a data-driven approach. In: Frontiers of Structural and Civil Engineering. Springer (2017)

    Google Scholar 

  29. Mosavi, A., Varkonyi-Koczy, A.R.: Integration of machine learning and optimization for robot learning. Adv. Intell. Syst. Comput. 519, 349–355 (2017)

    Google Scholar 

  30. Mosavi, A., Rabczuk, T.: Learning and intelligent optimization for computational materials design innovation. In: Learning and Intelligent Optimization. Springer (2017)

    Google Scholar 

  31. Mosavi, A.: Decision-making in complicated geometrical problems. Int. J. Comput. Appl. 87, 22–25 (2014)

    Google Scholar 

  32. Mosavi, A.: Reconsidering the multiple criteria decision making problems of construction workers with the aid of Grapheur. In: ANSYS and EnginSoft (2011)

    Google Scholar 

  33. Xiong, W.: Design and accelerated insertion of materials. Comp. Mater. 2, 150–159 (2016)

    Google Scholar 

  34. Chou, J.: Machine learning in concrete strength simulations: multi-nation data analytics. Constr. Build. Mater. 73, 771–780 (2014)

    Article  Google Scholar 

  35. Shandiz, M., Gauvin, R.: Application of machine learning methods for the prediction of crystal system of cathode materials. Comput. Mater. Sci. 117, 270–278 (2016)

    Article  Google Scholar 

  36. Kirchdoerfer, T., Ortiz, M.: Data-driven computational mechanics. Comput. Methods Appl. Mech. Eng. 304, 81–101 (2016)

    Article  MathSciNet  Google Scholar 

  37. Takahashi, K., Tanaka, Y.: Material synthesis and design from first principle calculations and machine learning. Comput. Mater. Sci. 112, 364–367 (2016)

    Article  Google Scholar 

  38. Khan, A.: Correlating dynamical mechanical properties with temperature. Comput. Mater. Sci. 45, 257–265 (2009)

    Article  Google Scholar 

  39. Pierro, M.: Multi-model ensemble for day ahead prediction of photovoltaic power generation. Sol. Energy 134, 132–146 (2016)

    Article  Google Scholar 

  40. Panchal, J.: Computational modeling in materials engineering. Design 45, 4–25 (2013)

    Google Scholar 

  41. Wang, X.: Human breath-print identification by E-nose, using information-theoretic feature selection prior to classification. Sens. Actuators 217, 165–174 (2015)

    Article  Google Scholar 

  42. DeCost, B., Holm, E.: A computer vision approach for automated analysis and classification of microstructural image data. Comput. Mater. Sci. 110, 126–133 (2015)

    Article  Google Scholar 

  43. Nanthakumar, S., Valizadeh, N., Park, H., Rabczuk, T.: Surface effects on shape and topology optimization of nanostructures. Comput. Mech. 56, 97–112 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  44. McKinsey, G.I.: Big Data: The Next Frontier for Innovation, Competition and Productivity. McKinsey Global Institute (2011)

    Google Scholar 

  45. Hamdia, K.: Predicting the fracture toughness of PNCs. MatScience 102, 304–313 (2015)

    Google Scholar 

  46. Mosavi, A., Vaezipour, A.: Developing effective tools for predictive analytics and informed decisions. Technical report (2013). https://doi.org/10.13140/RG.2.2.23902.84800

  47. Kotthaus, H.: Machine learning R programs. Comput. Simul. 85, 14–29 (2015)

    Article  MathSciNet  Google Scholar 

  48. Brunato, M., Battiti, R.: Grapheur: a software architecture for reactive and interactive optimization. In: Learning and Intelligent Optimization, pp. 232–246 (2010)

    Google Scholar 

Download references

Acknowledgement

This work has been sponsored by the Research & Development Program for the project “Modernization and Improvement of Technical Infrastructure for Research and Development of J. Selye University in the Fields of Nanotechnology and Intelligent Space”, ITMS 26210120042, co-funded by the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Institute of Structural Mechanics, Bauhaus University Weimar, Weimar, Germany

    Amir Mosavi & Timon Rabczuk

  2. Institute of Automation, Obuda University, Becsi Way 94-96, Budapest, 1034, Hungary

    Amir Mosavi & Annamária R. Varkonyi-Koczy

  3. Department of Mathematics and Informatics, J. Selye University, 945 01, Komarno, Slovakia

    Annamária R. Varkonyi-Koczy

Authors
  1. Amir Mosavi
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  2. Timon Rabczuk
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  3. Annamária R. Varkonyi-Koczy
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Corresponding author

Correspondence to Amir Mosavi .

Editor information

Editors and Affiliations

  1. Faculty of Physics, Iasi Plasma Advanced Research Center, Alexandru Ioan Cuza University of Iasi, Iasi, Romania

    Dumitru Luca

  2. Faculty of Physics, Iasi Plasma Advanced Research Center, Alexandru Ioan Cuza University of Iasi, Iasi, Romania

    Lucel Sirghi

  3. Faculty of Physics, Iasi Plasma Advanced Research Center, Alexandru Ioan Cuza University of Iasi, Iasi, Romania

    Claudiu Costin

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Mosavi, A., Rabczuk, T., Varkonyi-Koczy, A.R. (2018). Reviewing the Novel Machine Learning Tools for Materials Design. In: Luca, D., Sirghi, L., Costin, C. (eds) Recent Advances in Technology Research and Education. INTER-ACADEMIA 2017. Advances in Intelligent Systems and Computing, vol 660. Springer, Cham. https://doi.org/10.1007/978-3-319-67459-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-67459-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67458-2

  • Online ISBN: 978-3-319-67459-9

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