Material Agnostic Data-Driven Framework to Develop Structure-Property Linkages
265
linkages. Each step of the workflow allows selection and utilization of readily
accessible codes from a large library of repositories.
Emerging toolsets in materials data science and informatics have demonstrated
tremendous promise in addressing some of the key challenges in materials engineering. It is now possible to generate a large ensemble of datasets (inputs and
outputs) from a simulation toolset and publicly share these with the broader
scientific community in an open access data repository. Once this is accomplished,
it is possible to engage the broader scientific community in the extraction of the
embedded knowledge of these datasets. If this activity is guided in a suitable
framework for P-S-P, it could lead to an accelerated and robust curation of the
knowledge, while simultaneously ensuring the highest levels of access, sharing, and
dissemination for reuse.
References
1. National Research Council, Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security (The National
Academies Press, Washington, DC, 2008), p. 152
2. C. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics),
1st edn. (Springer-Verlag, New York, 2006), p. XX, 738
3. N.H. Paulson et al., Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics. Acta Mater. 129, 428–438 (2017)
4. Z. Yang et al., Deep learning approaches for mining structure-property linkages in high contrast
composites from simulation datasets. Comput. Mater. Sci. 151, 278–287 (2018)
5. A. Gupta et al., Structure–property linkages using a data science approach: application to a
non-metallic inclusion/steel composite system. Acta Mater. 91, 239–254 (2015)
6. A. Çeçen et al., A data-driven approach to establishing microstructure–property relationships
in porous transport layers of polymer electrolyte fuel cells. J. Power Sources 245, 144–153
(2014)
7. A.J. Schwartz, M. Kumar, B.L. Adams, D.P. Field, Electron Backscatter Diffraction in
Materials Science, 2nd edn. (Springer, US, 2009), p. XXII, 403
8. A.P. Lyubartsev, A. Laaksonen, Calculation of effective interaction potentials from radial
distribution functions: a reverse Monte Carlo approach. Phys. Rev. E 52(4), 3730 (1995)
9. B.S. Fromm et al., Grain size and orientation distributions: application to yielding of αtitanium. Acta Mater. 57(8), 2339–2348 (2009)
10. A. Cecen et al., 3-D microstructure analysis of fuel cell materials: spatial distributions of
tortuosity, void size and diffusivity. J. Electrochem. Soc. 159(3), B299–B307 (2012)
11. S.R. Kalidindi, S.R. Niezgoda, A.A. Salem, Microstructure informatics using higher-order
statistics and efficient data-mining protocols. JOM 63(4), 34–41 (2011)
12. S.R. Kalidindi, J.R. Houskamp, Application of the spectral methods of microstructure design
to continuous fiber-reinforced composites. J. Compos. Mater. 41(8), 909–930 (2007)
13. S. Torquato, H. Haslach, Random Heterogeneous Materials: Microstructure and Macroscopic
Properties (American Society of Mechanical Engineers Digital Collection, 2002)
14. N. Hansen, Hall–Petch relation and boundary strengthening. Scr. Mater. 51(8), 801–806 (2004)
15. A. Cord, F. Bach, D. Jeulin, Texture classification by statistical learning from morphological
image processing: application to metallic surfaces. J. Microsc. 239(2), 159–166 (2010)
16. A.A. Wheeler, W.J. Boettinger, G.B. McFadden, Phase-field model for isothermal phase
transitions in binary alloys. Phys. Rev. A 45(10), 7424 (1992)
265
linkages. Each step of the workflow allows selection and utilization of readily
accessible codes from a large library of repositories.
Emerging toolsets in materials data science and informatics have demonstrated
tremendous promise in addressing some of the key challenges in materials engineering. It is now possible to generate a large ensemble of datasets (inputs and
outputs) from a simulation toolset and publicly share these with the broader
scientific community in an open access data repository. Once this is accomplished,
it is possible to engage the broader scientific community in the extraction of the
embedded knowledge of these datasets. If this activity is guided in a suitable
framework for P-S-P, it could lead to an accelerated and robust curation of the
knowledge, while simultaneously ensuring the highest levels of access, sharing, and
dissemination for reuse.
References
1. National Research Council, Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security (The National
Academies Press, Washington, DC, 2008), p. 152
2. C. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics),
1st edn. (Springer-Verlag, New York, 2006), p. XX, 738
3. N.H. Paulson et al., Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics. Acta Mater. 129, 428–438 (2017)
4. Z. Yang et al., Deep learning approaches for mining structure-property linkages in high contrast
composites from simulation datasets. Comput. Mater. Sci. 151, 278–287 (2018)
5. A. Gupta et al., Structure–property linkages using a data science approach: application to a
non-metallic inclusion/steel composite system. Acta Mater. 91, 239–254 (2015)
6. A. Çeçen et al., A data-driven approach to establishing microstructure–property relationships
in porous transport layers of polymer electrolyte fuel cells. J. Power Sources 245, 144–153
(2014)
7. A.J. Schwartz, M. Kumar, B.L. Adams, D.P. Field, Electron Backscatter Diffraction in
Materials Science, 2nd edn. (Springer, US, 2009), p. XXII, 403
8. A.P. Lyubartsev, A. Laaksonen, Calculation of effective interaction potentials from radial
distribution functions: a reverse Monte Carlo approach. Phys. Rev. E 52(4), 3730 (1995)
9. B.S. Fromm et al., Grain size and orientation distributions: application to yielding of αtitanium. Acta Mater. 57(8), 2339–2348 (2009)
10. A. Cecen et al., 3-D microstructure analysis of fuel cell materials: spatial distributions of
tortuosity, void size and diffusivity. J. Electrochem. Soc. 159(3), B299–B307 (2012)
11. S.R. Kalidindi, S.R. Niezgoda, A.A. Salem, Microstructure informatics using higher-order
statistics and efficient data-mining protocols. JOM 63(4), 34–41 (2011)
12. S.R. Kalidindi, J.R. Houskamp, Application of the spectral methods of microstructure design
to continuous fiber-reinforced composites. J. Compos. Mater. 41(8), 909–930 (2007)
13. S. Torquato, H. Haslach, Random Heterogeneous Materials: Microstructure and Macroscopic
Properties (American Society of Mechanical Engineers Digital Collection, 2002)
14. N. Hansen, Hall–Petch relation and boundary strengthening. Scr. Mater. 51(8), 801–806 (2004)
15. A. Cord, F. Bach, D. Jeulin, Texture classification by statistical learning from morphological
image processing: application to metallic surfaces. J. Microsc. 239(2), 159–166 (2010)
16. A.A. Wheeler, W.J. Boettinger, G.B. McFadden, Phase-field model for isothermal phase
transitions in binary alloys. Phys. Rev. A 45(10), 7424 (1992)