Advertisement

Materials Data Infrastructure and Materials Informatics

  • Joanne Hill
  • Arun Mannodi-Kanakkithodi
  • Ramamurthy Ramprasad
  • Bryce Meredig
Chapter

Abstract

Data-driven materials research requires two key supporting components: data infrastructure and informatics. In this chapter, we review the state of the art in materials data infrastructure, focusing in detail on four infrastructure projects spanning academia, government, and industry. We also discuss data standards as an enabling step on the path to community-scale materials data infrastructure. We then introduce materials informatics as a potent accelerator of materials development and highlight specific application areas, including polymer dielectrics and dielectric breakdown.

Keywords

Materials data infrastructure Materials informatics Machine learning Data mining Data standards 

References

  1. 1.
    Westbrook, J.H., Rumble, J.R. Jr. Computerized Materials Data Systems. Gaithsburg (1983) https://www.osti.gov/scitech/biblio/6969565
  2. 2.
    O’Mara, J., Meredig, B., Michel, K.: Materials data infrastructure: a case study of the citrination platform to examine data import, storage, and access. JOM 68(8) 2013–2034 (2016)Google Scholar
  3. 3.
    Meredig, B.: Industrial materials informatics: analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain. COSSMS. 21(3), 159–166 (2016)Google Scholar
  4. 4.
    Frantzen, A., Sanders, D., Scheidtmann, J., Simon, U., Maier, W.F.: A flexible database for combinatorial and high-throughput materials science. QSAR Comb. Sci. 24(1), 22–28 (2005)CrossRefGoogle Scholar
  5. 5.
    Xu, Y., Yamazaki, M., Villars, P.: Inorganic materials database for exploring the nature of material. Jpn. J. Appl. Phys. 50(11), 11RH02 (2011)CrossRefGoogle Scholar
  6. 6.
    National Science and Technology Council Committee on Technology: Materials Genome Initiative Strategic Plan,” no. June, (2014)Google Scholar
  7. 7.
    Jain, A., Ong, S.P., Hautier, G., Chen, W., Richards, W.D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., Persson, K.A.: Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1(1), 11002 (2013)CrossRefGoogle Scholar
  8. 8.
    Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R.H., Nelson, L.J., Hart, G.L.W., Sanvito, S., Buongiorno-Nardelli, M., Mingo, N., Levy, O.: AFLOWLIB.ORG: a distributed materials properties repository from high-throughput ab initio calculations. Comput. Mater. Sci. 58, 227–235 (2012)CrossRefGoogle Scholar
  9. 9.
    Saal, J.E., Kirklin, S., Aykol, M., Meredig, B., Wolverton, C.: Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD). JOM. 65(11), 1501–1509 (2013)CrossRefGoogle Scholar
  10. 10.
    Holdren, J.P.: Memorandum for the Heads of Executive Departments and Agencies: Increasing Access to the Results of Federally Funded Scientific Research. pp. 1–6, (2013)Google Scholar
  11. 11.
    Austin, T.: No Title. Mater. Discov. (2016)Google Scholar
  12. 12.
    The NoMaD Repository. [Online]. Available: http://nomad-repository.eu/cms/. Accessed: 17-Jul-2016
  13. 13.
    Hill, J., Mulholland, G., Pearson, K., Seshadri, R., Wolverton, C., Meredig, B.: Materials science with large scale data and informatics: unlocking new opportunities. MRS Bull. 41, 399–409 (2016)CrossRefGoogle Scholar
  14. 14.
    NIST Repositories.Google Scholar
  15. 15.
    Foster, I., Ananthakrishnan, R., Blaiszik, B., Chard, K., Osborn, R., Tuecke, S., Wilde, M., Wozniak, J.: Networking materials data: accelerating discovery at an experimental facility. Adv. Parallel Comput. 26, (2015)Google Scholar
  16. 16.
    Inorganic Crystal Structure Database. [Online]. Available: https://lib.stanford.edu/inorganic-crystal-structure-database-icsd. Accessed: 09-Feb-2015
  17. 17.
    A. Belsky, M. Hellenbrandt, V. L. Karen, P. Luksch, New developments in the inorganic crystal structure database (ICSD): accessibility in support of materials research and design, Acta Crystallogr. Sect. B Struct. Sci., 58, 3, 364–369,2002Google Scholar
  18. 18.
    Meredig, B.: Industrial materials informatics: analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain. COSSMS (2016)Google Scholar
  19. 19.
    Codd, E.F.: Relational database: a practical foundation for productivity. Commun. ACM. 25(2), 109–117 (1982)CrossRefGoogle Scholar
  20. 20.
    Sumathi, S., Esakkirajan, S.: Fundamentals of Relational Database Management SystemsGoogle Scholar
  21. 21.
    Sadalage, P.J., Fowler, M.: NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley, Upper Saddle River (2013)Google Scholar
  22. 22.
    Blair, J., Canon, R.S., Deslippe, J., Essiari, A., Hexemer, A., MacDowell, A.A., Parkinson, D.Y., Patton, S.J., Ramakrishnan, L., Tamura, N., Tierney, B.L., Tull, C.E.: High performance data management and analysis for tomography, p. 92121G (2014)Google Scholar
  23. 23.
    Mesnier, M., Ganger, G.R., Riedel, E.: Storage area networking - object-based storage. IEEE Commun. Mag. 41(8), 84–90 (2003)CrossRefGoogle Scholar
  24. 24.
    Hall, S.R., Allen, F.H., Brown, I.D.: The crystallographic information file (CIF): a new standard archive file for crystallography. Acta Crystallogr. Sect. A Found. Crystallogr. 47(6), 655–685 (1991)CrossRefGoogle Scholar
  25. 25.
    Warren, J.A, Boisvert, R.F.: Building the Materials Innovation Infrastructure: Data and Standards Building the Materials Innovation Infrastructure: Data and Standards. (2012)Google Scholar
  26. 26.
    Ward, C.H., Warren, J.A., Ward, C.H.: Materials Genome Initiative : Materials DataGoogle Scholar
  27. 27.
    NIST Materials Data Curation System. [Online]. Available: https://mgi.nist.gov/materials-data-curation-system
  28. 28.
    Huck, P., Jain, A., Gunter, D., Winston, D., Persson, K.: A Community Contribution Framework for Sharing Materials Data with Materials Project. (2015)Google Scholar
  29. 29.
    Citrine Informatics, “Citrination.” [Online]. Available: https://citrination.com. Accessed: 09-Feb-2015
  30. 30.
    Michel, K.J., Meredig, B.: Beyond bulk Single crystals: a data format for all materials structure-property-processing relationships. MRS Bull. 41(8), 617–623 (2016)Google Scholar
  31. 31.
    Documenation of the Physical Information File (PIF) schema. [Online]. Available: http://citrineinformatics.github.io/pif-documentation/
  32. 32.
    Mulholland, G.J., Paradiso, S.P.: Perspective: materials informatics across the product lifecycle: selection, manufacturing, and certification. APL Mater. 4(5), 53207 (2016)CrossRefGoogle Scholar
  33. 33.
  34. 34.
  35. 35.
  36. 36.
  37. 37.
  38. 38.
  39. 39.
    Citrination API DocumentationGoogle Scholar
  40. 40.
    Seshadri, R., Sparks, T.D.: Perspective: interactive material property databases through aggregation of literature data. APL Mater. 4(5), 53206 (2016)CrossRefGoogle Scholar
  41. 41.
    Shin, J., Wu, S., Wang, F., De Sa, C., Zhang, C., Ré, C.: Incremental knowledge base construction using DeepDive. Proc. VLDB Endow. 8(11), 1310–1321 (2015)CrossRefGoogle Scholar
  42. 42.
    Lucene. [Online]. Available: https://lucene.apache.org/
  43. 43.
    Solr. (n.a.) [Online]. Available: http://lucene.apache.org/solr
  44. 44.
    ElasticSearch. (n.a.) [Online]. Available: https://www.elastic.co/products/elasticsearch
  45. 45.
    Dima, A., Bhaskarla, S., Becker, C., Brady, M., Campbell, C., Dessauw, P., Hanisch, R., Kattner, U., Kroenlein, K., Newrock, M., Peskin, A., Plante, R., Li, S.-Y., Rigodiat, P.-F., Amaral, G. S., Trautt, Z., Schmitt, X., Warren, J., Youssef, S : Informatics infrastructure for the materials genome initiative. JOM. (2016)Google Scholar
  46. 46.
    Blaiszik, B., Chard, K., Pruyne, J., Ananthakrishnan, R., Tuecke, S., Foster, I.: The materials data facility: data services to advance materials science research. JOM. 68(8), 2045–2052 (2016)CrossRefGoogle Scholar
  47. 47.
    Tansley, S., Tolle, K.: The Fourth Paradigm: Data-Intensive Scientific DiscoveryGoogle Scholar
  48. 48.
    White, A.: The materials genome initiative: one year on. MRS Bull. 37(8), 715–716 (2012)CrossRefGoogle Scholar
  49. 49.
    Materials in the New Millennium: National Academies Press: Washington, D.C (2001)Google Scholar
  50. 50.
    Eagar, Thomas: Bringing new materials to market. Technol. Rev. 98(2), (1995)Google Scholar
  51. 51.
    Nakamura, S., Krames, M.R.: History of Gallium–Nitride-Based Light-Emitting Diodes for IlluminationGoogle Scholar
  52. 52.
    Hadjipanayis, G.C., Hazelton, R.C., Lawless, K.R.: New iron-rare-earth based permanent magnet materials. Appl. Phys. Lett. 43(8), 797 (1983)CrossRefGoogle Scholar
  53. 53.
    Ceder, G., Whittingham, M.S., Ceder, G., Van der Ven, A., Morgan, D., Van der Ven, A., Ceder, G., Kang, B., Ceder, G., Ping Ong, S., Wang, L., Kang, B., Ceder, G., Kayyar, A., Qian, H., Luo, J., Ong, S.P., Jain, A., Hautier, G., Kang, B., Ceder, G., Reed, J., Ceder, G., Reed, J., Ceder, G.: Opportunities and challenges for first-principles materials design and applications to li battery materials. MRS Bull. 35(9), 693–701 (2010)CrossRefGoogle Scholar
  54. 54.
    Allison, J., Backman, D., Christodoulou, L.: Integrated computational materials engineering: a new paradigm for the global materials profession. JOM. 58(11), 25–27 (2006)CrossRefGoogle Scholar
  55. 55.
    Johnson, R.C.: IBM launches accelerated discovery lab. EE Times (2013)Google Scholar
  56. 56.
    Suh, C., Rajan, K., Vogel, B., Narasimhan, B., Mallapragada, S.: Informatics Methods for Combinatorial Materials Science. Wiley, Hoboken (2006)Google Scholar
  57. 57.
    Agrawal, A., Deshpande, P.D., Cecen, A., Basavarsu, G.P., Choudhary, A.N., Kalidindi, S.R.: Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integr. Mater. Manuf. Innov. 3(1), 8 (2014)CrossRefGoogle Scholar
  58. 58.
    Jee, D.-H., Kang, K.-J.: A method for optimal material selection aided with decision making theory. Mater. Des. 21(3), 199–206 (2000)CrossRefGoogle Scholar
  59. 59.
    Sparks, T.D., Gaultois, M.W., Oliynyk, A., Brgoch, J., Meredig, B.: Data mining our way to the next generation of thermoelectrics. Scr. Mater. (2015)Google Scholar
  60. 60.
    Gaultois, M.W., Oliynyk, A.O., Mar, A., Sparks, T.D., Mulholland, G.J., Meredig, B.: Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Mater. 4(5), 53213 (2016)CrossRefGoogle Scholar
  61. 61.
    Peterson, A.A., Christensenb, R., Khorshidia, A.: Addressing uncertainty in atomistic machine learning. Phys. Chem. Chem. Phys. (18), 10978–10985 (2017)Google Scholar
  62. 62.
    Jain, A., Hautier, G., Moore, C.J., Ping Ong, S., Fischer, C.C., Mueller, T., Persson, K.A., Ceder, G.: A high-throughput infrastructure for density functional theory calculations. Comput. Mater. Sci. 50(8), 2295–2310 (2011)CrossRefGoogle Scholar
  63. 63.
    Eager, T.W.: No Title. MIT Technol. Rev. 98(42), (1995)Google Scholar
  64. 64.
    Barnett, B., Bowen, H.K., Clark, K.: The changing paradigm for business success in advanced materials and components manufacturing. MRS Bull. 17(4), 35–37 (1992)CrossRefGoogle Scholar
  65. 65.
    Swink, M., Song, M.: Effects of marketing-manufacturing integration on new product development time and competitive advantage. J. Oper. Manag. 25(1), 203–217 (2007)CrossRefGoogle Scholar
  66. 66.
    Meredig, B., Agrawal, A., Kirklin, S., Saal, J.E., Doak, J.W., Thompson, A., Zhang, K., Choudhary, A., Wolverton, C.: Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B. 89(9), 94104 (2014)CrossRefGoogle Scholar
  67. 67.
    Faber, F., Lindmaa, A., von Lilienfeld, O.A., Armiento, R.: Crystal Structure Representations for Machine Learning Models of Formation Energies (2015)Google Scholar
  68. 68.
    Balachandran, P.V., Theiler, J., Rondinelli, J.M., Lookman, T.: Materials prediction via classification learning. Sci Rep. 5, 13285 (2015)CrossRefGoogle Scholar
  69. 69.
    Kong, C.S., Broderick, S.R., Jones, T.E., Loyola, C., Eberhart, M.E., Rajan, K.: Mining for elastic constants of intermetallics from the charge density landscape. Phys. B Condens. Matter. 458, 1–7 (2015)CrossRefGoogle Scholar
  70. 70.
    Kappes, B.B., Ciobanu, C.V.: Materials and Manufacturing Processes Materials Screening Through GPU Accelerated Topological MappingGoogle Scholar
  71. 71.
    Fischer, C.C., Tibbetts, K.J., Morgan, D., Ceder, G.: Predicting crystal structure by merging data mining with quantum mechanics. Nat. Mater. 5(8), 641–646 (2006)CrossRefGoogle Scholar
  72. 72.
    Pyzer-Knapp, E.O., Suh, C., Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Aspuru-Guzik, A.: What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery.  https://doi.org/10.1146/annurev-matsci-070214-020823, (2015)
  73. 73.
    Isayev, O., Fourches, D., Muratov, E.N., Oses, C., Rasch, K., Tropsha, A., Curtarolo, S.: Materials cartography: representing and mining materials space using structural and electronic fingerprints. Chem. Mater. 27(3), 735–743 (2015)CrossRefGoogle Scholar
  74. 74.
    von Lilienfeld, O.A., Ramakrishnan, R., Rupp, M., Knoll, A.: Fourier series of atomic radial distribution functions: a molecular fingerprint for machine learning models of quantum chemical properties. Int. J. Quantum Chem. 115(16), 1084–1093 (2015)CrossRefGoogle Scholar
  75. 75.
    Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O.A., Müller, K.-R., Tkatchenko, A.: Machine learning predictions of molecular properties: accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett. 6(12), 2326–2331 (2015)CrossRefGoogle Scholar
  76. 76.
    Sarkar, N.: The combined contraceptive vaginal device (NuvaRing®): A comprehensive review.  https://doi.org/10.1080/13625180500131683, (2009)
  77. 77.
    Sirisalee, P., Ashby, M.F., Parks, G.T., Clarkson, P.J.: Multi-criteria material selection in engineering design. Adv. Eng. Mater. 6(12), 84–92 (2004)CrossRefGoogle Scholar
  78. 78.
    Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization *Google Scholar
  79. 79.
    Sharma, V., Wang, C., Lorenzini, R.G., Ma, R., Zhu, Q., Sinkovits, D.W., Pilania, G., Oganov, A.R., Kumar, S., Sotzing, G.A., Boggs, S.A., Ramprasad, R.: Rational design of all organic polymer dielectrics. Nat. Commun. 5, 4845 (2014)CrossRefGoogle Scholar
  80. 80.
    Mannodi-Kanakkithodi, A., Pilania, G., Huan, T.D., Lookman, T., Ramprasad, R.: Machine learning strategy for accelerated design of polymer dielectrics. Sci Rep. 6, 20952 (2016)CrossRefGoogle Scholar
  81. 81.
    Goedecker, S.: Minima hopping: an efficient search method for the global minimum of the potential energy surface of complex molecular systems. J. Chem. Phys. 120(21), 9911–9917 (2004)CrossRefGoogle Scholar
  82. 82.
    Kresse, G., Hafner, J.: Ab initio molecular dynamics for liquid metals. Phys. Rev. B. 47(1), 558–561 (1993)CrossRefGoogle Scholar
  83. 83.
    Heyd, J., Scuseria, G.E., Ernzerhof, M.: Hybrid functionals based on a screened coulomb potential. J. Chem. Phys. 118(18), 8207 (2003)CrossRefGoogle Scholar
  84. 84.
    Baroni, S., de Gironcoli, S., Dal Corso, A., Giannozzi, P.: Phonons and related crystal properties from density-functional perturbation theory. Rev. Mod. Phys. 73(2), 515–562 (2001)CrossRefGoogle Scholar
  85. 85.
    Mannodi-Kanakkithodi, A., Treich, G. M., Huan, T. D., Ma, R., Tefferi, M., Cao, Y., Sotzing, G. A., Ramprasad, R.: Rational co-design of polymer dielectrics for energy storage. Adv. Mater. (2016)Google Scholar
  86. 86.
    Huan, T.D., Mannodi-Kanakkithodi, A., Kim, C., Sharma, V., Pilania, G., Ramprasad, R.: A polymer dataset for accelerated property prediction and design. Sci. Data. 3, 160012 (2016)CrossRefGoogle Scholar
  87. 87.
    Vu, K., Snyder, J.C., Li, L., Rupp, M., Chen, B.F., Khelif, T., Müller, K.-R., Burke, K.: Understanding kernel ridge regression: common behaviors from simple functions to density functionals. Int. J. Quantum Chem. 115(16), 1115–1128 (2015)CrossRefGoogle Scholar
  88. 88.
    Kim, C., Pilania, G., Ramprasad, R.: From organized high-throughput data to phenomenological theory using machine learning: the example of dielectric breakdown. Chem. Mater. 28, 1304–1311 (2016)CrossRefGoogle Scholar
  89. 89.
    Fröhlich, H.: Theory of dielectric breakdown. Nature. 151(3829), 339–340 (1943)CrossRefGoogle Scholar
  90. 90.
    Frohlich, H.: On the theory of dielectric breakdown in solids. Proc. R. Soc. A Math. Phys. Eng. Sci. 188(1015), 521–532 (1947)CrossRefGoogle Scholar
  91. 91.
    Sun, Y., Boggs, S.A., Ramprasad, R.: The intrinsic electrical breakdown strength of insulators from first principles. Appl. Phys. Lett. 101(13), 132906 (2012)CrossRefGoogle Scholar
  92. 92.
    Kim, C., Pilania, G., Ramprasad, R.: Machine learning assisted predictions of intrinsic dielectric breakdown strength of ABX 3 perovskites. J. Phys. Chem. C. 120(27), 14575–14580 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Joanne Hill
    • 1
  • Arun Mannodi-Kanakkithodi
    • 2
  • Ramamurthy Ramprasad
    • 2
  • Bryce Meredig
    • 1
  1. 1.Citrine InformaticsRedwood CityUSA
  2. 2.Department of Materials Science and EngineeringUniversity of ConnecticutStorrsUSA

Personalised recommendations