Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Hydrothermal Synthesis and Characterization of Mixed Fluoride Based Nanophosphors Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Reviewing the Novel Machine Learning Tools for Materials Design

verfasst von : Amir Mosavi, Timon Rabczuk, Annamária R. Varkonyi-Koczy

Erschienen in: Recent Advances in Technology Research and Education

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Interaction of Hydrogen Isotopes with Radiation Damaged Tungsten
Nächstes Kapitel Sol-gel Preparation and Luminescent Properties of Transparent Thin Films and Ceramics of ZrO2:Ti3+
Literatur
1.
Artrith, N., Urban, A.: An implementation of artificial neural-network potentials for atomistic materials simulations. Comput. Mater. Sci. 114, 135–150 (2016)CrossRef
2.
3.
Battiti, R., Brunato, M.: The LION Way: Machine Learning, Lionlab (2015)
4.
Brunato, M., Battiti, R.: Learning and intelligent optimization: one ring to rule them all. Proc. VLDB Endow. 6, 1176–1177 (2013)CrossRef
5.
6.
Ceder, G.: Challenges for materials design. Mater. Res. 35, 693–701 (2010)
7.
Fischer, C.: Predicting crystal structure by merging data mining. Nature 5, 641–646 (2006)CrossRef
8.
Jain, A.: A high-throughput infrastructure for density functional theory calculations. Comp. Mater. Sci. 50, 2295–2310 (2011)CrossRef
9.
Nanthakumar, S., Zhuang, X., Park, H., Rabczuk, T.: Topology optimization of flexoelectric structures. J. Mech. Phys. Solids 105, 217–234 (2017)MathSciNetCrossRef
10.
Lencer, D.: A map for phase-change materials. Nat. Mater. 7, 972–977 (2008)CrossRef
11.
Mosavi, A.: Decision-making software architecture; the visualization and data mining assisted approach. Inf. Comput. Sci. 3, 12–26 (2014)
12.
Milani, A.: Multiple criteria decision making with life cycle assessment for material selection of composites. Express Polym. Lett. 5, 1062–1074 (2011)CrossRef
13.
Mosavi, A., Vaezipour, A.: Reactive search optimization; application to multiobjective optimization problems. Appl. Math. 3, 1572–1582 (2012)CrossRef
14.
Mosavi, A.: A multicriteria decision making environment for engineering design and production decision-making. Int. J. Comput. Appl. 69, 26–38 (2013)
15.
Mosavi, A.: Decision-making in complicated geometrical problems. Int. J. Comput. Appl. 87, 22–25 (2014)
16.
Mosavi, A., Varkonyi, A.: Learning in robotics. Int. J. Comput. Appl. 157, 8–11 (2017)
17.
Mcdowell, D.: Simulation-assisted design of materials. Microstruct. 65, 617–647 (2010)
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)
19.
Saito, T.: Computational materials design, vol. 34. Springer Science & Business Media, Heidelberg (2013)
20.
Stucke, D.: Predictions of new crystalline states. Nano Lett. 3, 1183–1186 (2003)CrossRef
21.
Mosavi, A.: Optimal engineering design. Technical report, University of Debrecen (2013)
22.
Curtarolo, S.: High-throughput computational materials design. Nature 12, 191–201 (2013)CrossRef
23.
Setyawan, W., Curtarolo, S.: High-throughput electronic band structure calculations: challenges and tools. Comp. Mater. Sci. 49, 299–312 (2010)CrossRef
24.
Kolmogorov, A.: Prediction of new crystal structure phases. Phys. Rev. 73, 180–195 (2006)CrossRef
25.
26.
Levy, O.: Uncovering compounds by synergy of cluster expansion and high-throughput methods. J. Am. Chem. Soc. 132, 4830–4833 (2010)CrossRef
27.
Bhadeshia, H.: Neural networks in materials science. Mater. Sci. 25, 504–510 (2009)MathSciNet
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)
29.
Mosavi, A., Varkonyi-Koczy, A.R.: Integration of machine learning and optimization for robot learning. Adv. Intell. Syst. Comput. 519, 349–355 (2017)
30.
Mosavi, A., Rabczuk, T.: Learning and intelligent optimization for computational materials design innovation. In: Learning and Intelligent Optimization. Springer (2017)
31.
Mosavi, A.: Decision-making in complicated geometrical problems. Int. J. Comput. Appl. 87, 22–25 (2014)
32.
Mosavi, A.: Reconsidering the multiple criteria decision making problems of construction workers with the aid of Grapheur. In: ANSYS and EnginSoft (2011)
33.
Xiong, W.: Design and accelerated insertion of materials. Comp. Mater. 2, 150–159 (2016)
34.
Chou, J.: Machine learning in concrete strength simulations: multi-nation data analytics. Constr. Build. Mater. 73, 771–780 (2014)CrossRef
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)CrossRef
36.
Kirchdoerfer, T., Ortiz, M.: Data-driven computational mechanics. Comput. Methods Appl. Mech. Eng. 304, 81–101 (2016)MathSciNetCrossRef
37.
Takahashi, K., Tanaka, Y.: Material synthesis and design from first principle calculations and machine learning. Comput. Mater. Sci. 112, 364–367 (2016)CrossRef
38.
Khan, A.: Correlating dynamical mechanical properties with temperature. Comput. Mater. Sci. 45, 257–265 (2009)CrossRef
39.
Pierro, M.: Multi-model ensemble for day ahead prediction of photovoltaic power generation. Sol. Energy 134, 132–146 (2016)CrossRef
40.
Panchal, J.: Computational modeling in materials engineering. Design 45, 4–25 (2013)
41.
Wang, X.: Human breath-print identification by E-nose, using information-theoretic feature selection prior to classification. Sens. Actuators 217, 165–174 (2015)CrossRef
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)CrossRef
43.
Nanthakumar, S., Valizadeh, N., Park, H., Rabczuk, T.: Surface effects on shape and topology optimization of nanostructures. Comput. Mech. 56, 97–112 (2015)MathSciNetCrossRefMATH
44.
McKinsey, G.I.: Big Data: The Next Frontier for Innovation, Competition and Productivity. McKinsey Global Institute (2011)
45.
Hamdia, K.: Predicting the fracture toughness of PNCs. MatScience 102, 304–313 (2015)
46.
47.
48.
Brunato, M., Battiti, R.: Grapheur: a software architecture for reactive and interactive optimization. In: Learning and Intelligent Optimization, pp. 232–246 (2010)
Metadaten
Titel
Reviewing the Novel Machine Learning Tools for Materials Design
verfasst von
Amir Mosavi
Timon Rabczuk
Annamária R. Varkonyi-Koczy
Copyright-Jahr
2018
Buch
Recent Advances in Technology Research and Education

Print ISBN: 978-3-319-67458-2

Electronic ISBN: 978-3-319-67459-9

Copyright-Jahr: 2018

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

Premium Partner

    Bildnachweise