Advertisement

Data Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN

  • 93 Accesses

Abstract

Materials data management, analytics, and e-collaborations have been identified as three of the main technological gaps currently hindering the realization of the accelerated development and deployment of advanced materials targeted by the federal materials genome initiative. In this paper, we present our ongoing efforts aimed at addressing these critical gaps through the customized design and build of suitable data infrastructure elements. Specifically, our solutions include: (1) ELA—an experimental and laboratory automation software platform that systematically tracks interrelationships between the heterogeneous experimental datasets (i.e., provenance) acquired from diverse sample preparation and materials characterization equipment in a single consistent metadata database, (2) PyMKS—the first Python-based open-source materials data analytics framework that can be used to create high-fidelity, reduced-order (i.e., low computational cost), process–structure–property linkages for a broad range of material systems with a rich hierarchy of internal structures spanning multiple length scales, and (3) MATIN—a HUBzero-based software platform aimed at nucleating an emergent e-science community at the intersection of materials science, manufacturing, and computer science, and facilitating highly productive digital collaborations among geographically and organizationally distributed materials innovation stakeholders. This paper provides a timely report of lessons learned from these interrelated efforts.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

References

  1. 1.

    National Science and Technology Council (2011) Materials genome initiative for global competitiveness

  2. 2.

    Olson GB, Kuehmann CJ (2014) Materials genomics: from CALPHAD to flight. Scr Mater 70:25–30

  3. 3.

    McDowell DL, Olson GB (2008) Concurrent design of hierarchical materials and structures. Sci Model Simul 15:207–240

  4. 4.

    Hao S et al (2003) A hierarchical multi-physics model for design of high toughness steels. J Comput Aided Mater Des 10:99–142

  5. 5.

    Olson GB (2006) Advances in theory: martensite by design. Mater Sci Eng, A 438:48–54

  6. 6.

    Adams BL, Kalidindi SR, Fullwood DT (2012) Microstructure sensitive design for performance optimization. Elsevier, Oxford

  7. 7.

    Fullwood DT et al (2010) Microstructure sensitive design for performance optimization. Prog Mater Sci 55(6):477–562

  8. 8.

    TMS (2017) Building a materials data infrastructure: opening new pathways to discovery and innovation in science and engineering. TMS, Pittsburgh, p xxvi,72

  9. 9.

    McDowell DL, Kalidindi SR (2016) The materials innovation ecosystem: a key enabler for the materials genome initiative. MRS Bull 41(04):326–337

  10. 10.

    Kalidindi SR (2015) Data science and cyberinfrastructure: critical enablers for accelerated development of hierarchical materials. Int Mater Rev 60(3):150–168

  11. 11.

    Kalidindi SR, Medford AJ, McDowell DL (2016) Vision for data and informatics in the future materials innovation ecosystem. JOM 68(8):2126–2137

  12. 12.

    Kalidindi SR, Graef MD (2015) Materials data science: current status and future outlook. Annu Rev Mater Res 45:171–193

  13. 13.

    O’Mara J, Meredig B, Michel K (2016) Materials data infrastructure: a case study of the citrination platform to examine data import, storage, and access. JOM 68(8):2031–2034

  14. 14.

    Dima A et al (2016) Informatics infrastructure for the materials genome initiative. JOM 68(8):2053–2064

  15. 15.

    Jain A, Persson KA, Ceder G (2016) Research update: the materials genome initiative: data sharing and the impact of collaborative ab initio databases. APL Mater 4(5):053102

  16. 16.

    Pfeif EA, Kroenlein K (2016) Perspective: data infrastructure for high throughput materials discovery. APL Mater 4(5):053203

  17. 17.

    Wilkinson MD, et al (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018

  18. 18.

    International DOI Foundation (IDF) (2018) Available from: https://www.doi.org/

  19. 19.

    HDL.NET® Information Services (2019) Available from: http://handle.net/

  20. 20.

    Dieter GE (ed) (1997) Materials selection and design. Cleveland, ASM International

  21. 21.

    Ashby MF, Greer AL (2006) Metallic glasses as structural materials. Scr Mater 54(3):321–326

  22. 22.

    Cahn RW, Haasen P (1996) Physical metallurgy. Elsevier, Oxford

  23. 23.

    Olson GB (2000) Designing a new material world. Science 288(5468):993

  24. 24.

    Olson GB (1997) Computational design of hierarchically structured materials. Science 277(29):1237–1242

  25. 25.

    Olson GB (1997) Systems design of hierarchically structured materials: advanced steels. J Comput-Aided Mater Des 4:143–156

  26. 26.

    McDowell DL et al (2009) Integrated design of multiscale, multifunctional materials and products. Elsevier, Oxford

  27. 27.

    Kalidindi SR (2015) Hierarchical materials informatics. Butterworth Heinemann, Oxford

  28. 28.

    Gomberg JA, Medford AJ, Kalidindi SR (2017) Extracting knowledge from molecular mechanics simulations of grain boundaries using machine learning. Acta Mater. 133(Supplement C):100–108

  29. 29.

    PRedictive Integrated Structural Materials Science (PRISMS). http://www.prisms-center.org/#/home

  30. 30.

    Materials Data Curation System (MDCS). https://mdcs.nist.gov/

  31. 31.

    Blaiszik B et al (2016) The materials data facility: data services to advance materials science research. JOM 68(8):2045–2052

  32. 32.

    The Materials Data Facility (MDF) (2019). https://materialsdatafacility.org/

  33. 33.

    Figshare. https://figshare.com

  34. 34.

    The Material Data Management Consortium (MDMC). www.mdmc.net

  35. 35.

    The Materials Experiment and Analysis Database (MEAD). https://solarfuelshub.org/materials-experiment-andanalysis-database

  36. 36.

    NREL Energy DataBUS. http://www.nrel.gov/analysis/databus/

  37. 37.

    Pendleton IM et al (2019) Experiment specification, capture and laboratory automation technology (ESCALATE): a software pipeline for automated chemical experimentation and data management. MRS Commun 2:1–14

  38. 38.

    Dryad Digital Repository (2019). https://datadryad.org

  39. 39.

    Vogt H (2002) Efficient object identification with passive RFID tags. In: International conference on pervasive computing. Springer

  40. 40.

    National Institute of Standards and Technology, G. Maryland. NIST schema repository and registry. https://schemas.nist.gov/

  41. 41.

    Garcia L, et al (2017) Bioschemas: schema.org for the life sciences. proceedings of SWAT4LS

  42. 42.

    Brough DB, Wheeler D, Kalidindi SR (2017) Materials knowledge systems in Python—a data science framework for accelerated development of hierarchical materials. Integr Mater Manuf Innov 6:36–53

  43. 43.

    The Materials Project. https://materialsproject.org

  44. 44.

    JARVIS (Joint Automated Repository for Various Integrated Simulations). https://jarvis.nist.gov/

  45. 45.

    The Novel Materials Discovery (NOMAD) Laboratory. https://nomad-coe.eu/

  46. 46.

    Torquato S (2013) Random heterogeneous materials: microstructure and macroscopic properties, vol 16. Springer, Berlin

  47. 47.

    Brough DB, Wheeler D, Kalidindi SR (2017) Materials knowledge systems in python—a data science framework for accelerated development of hierarchical materials. Integr Mater Manuf Innov 6(1):36–53

  48. 48.

    Kalidindi SR et al (2015) Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets. Nanotechnology 26(34):344006

  49. 49.

    Brough DB et al (2017) Extraction of process-structure evolution linkages from X-ray scattering measurements using dimensionality reduction and time series analysis. Integr Mater Manuf Innov 6(2):147–159

  50. 50.

    Brough DB et al (2017) Microstructure-based knowledge systems for capturing process-structure evolution linkages. Curr Opin Solid State Mater Sci 21(3):129–140

  51. 51.

    Popova E et al (2017) Process-structure linkages using a data science approach: application to simulated additive manufacturing data. Integr Mater Manuf Innov 6(1):54–68

  52. 52.

    Paulson NH et al (2017) Reduced-order structure-property linkages for polycrystalline microstructures based on 2-point statistics. Acta Mater 129:428–438

  53. 53.

    GitHub (2019). https://github.com/

  54. 54.

    Conda-Forge (2019). https://conda-forge.org/

  55. 55.

    Wheeler D, Brough DB (2017) PyMKS. http://pymks.org

  56. 56.

    Kowalczyk K, Gambin W (2004) Model of plastic anisotropy evolution with texture-dependent yield surface. Int J Plast 20(1):19–54

  57. 57.

    Team DD (2016) Dask: library for dynamic task scheduling. https://dask.org/

  58. 58.

    Wheeler D, Brough DB (2017) PyMKS examples. http://pymks.org/en/latest/rst/index.html

  59. 59.

    CI C (2019) Circle CI tutorials and sample apps. https://circleci.com/docs/2.0/tutorials/

  60. 60.

    Travis C (2018) Test and deploy your code with confidence. https://travis-ci.org/

  61. 61.

    Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res. 12:2825–2830

  62. 62.

    Materials Innovation Network (MATIN) (2019). https://matin.gatech.edu

  63. 63.

    HUBzero. [cited 2016 March 6]. https://hubzero.org/

  64. 64.

    Galaxy Project (2019). https://mygeohub.org/groups/gabbs/aboutidata

  65. 65.

    Globus (2019). https://www.globus.org/

  66. 66.

    iData—Data Management with Geospatial and Metadata Support (2019). https://mygeohub.org/groups/gabbs/aboutidata

  67. 67.

    The geospatial data analysis building blocks (GABBs) (2019). https://mygeohub.org/groups/gabbs/

  68. 68.

    Zhao L. et al. (2017) GABBs-reusable geospatial data analysis building blocks for science gateways. In: IWSG

  69. 69.

    gUSE grid and cloud science gateway (2019). https://sourceforge.net/projects/guse/

  70. 70.

    Apache Airavata (2016). https://airavata.apache.org/

  71. 71.

    Open Science Framework (OSF) (2019). https://osf.io/

  72. 72.

    CyVerse (2019). https://www.cyverse.org/

  73. 73.

    Goff SA et al (2011) The iPlant collaborative: cyberinfrastructure for plant biology. Front Plant Sci 2:34

  74. 74.

    Merchant N et al (2016) The iPlant collaborative: cyberinfrastructure for enabling data to discovery for the life sciences. PLoS Bio 14(1):e1002342

  75. 75.

    Partnership for an Advanced Computing Environment (PACE) (2019). https://pace.gatech.edu/

  76. 76.

    The Extreme Science and Engineering Discovery Environment (XSEDE). (2018). https://www.xsede.org/

  77. 77.

    The Center for Materials Design Development and Deployment (MD3) (2019). https://md3.gatech.edu/

  78. 78.

    Robo-Met Materials Characterization. https://www.ues.com/robomet

  79. 79.

    Autonomous Materials Discovery (AiMade). http://www.aimade.org/

  80. 80.

    ADA (2019). http://www.projectada.ca/

  81. 81.

    Nikolaev P et al (2016) Autonomy in materials research: a case study in carbon nanotube growth. Comput Mater 2(1):16031

  82. 82.

    Maruyama B, et al (2017) Autonomous experimentation applied to carbon nanotube synthesis. In: Meeting abstracts. The Electrochemical Society

  83. 83.

    Granda JM et al (2018) Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559(7714):377–381

  84. 84.

    Steiner S et al (2019) Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363(6423):eaav2211

  85. 85.

    Fitzpatrick DE, Battilocchio C, Ley SV (2016) A novel internet-based reaction monitoring, control and autonomous self-optimization platform for chemical synthesis. Org Process Res Dev 20(2):386–394

  86. 86.

    Cortés-Borda D et al (2018) An autonomous self-optimizing flow reactor for the synthesis of natural product carpanone. J Org Chem 83(23):14286–14299

  87. 87.

    Henson AB, Gromski PS, Cronin L (2018) Designing algorithms to aid discovery by chemical Robots. ACS Central Sci 4(7):793–804

  88. 88.

    Dragone V et al (2017) An autonomous organic reaction search engine for chemical reactivity. Nat Commun 8(1):15733

  89. 89.

    Kalidindi SR, Pathak S (2008) Determination of the effective zero-point and the extraction of spherical nanoindentation stress–strain curves. Acta Mater 56(14):3523–3532

  90. 90.

    Pathak S, Shaffer J, Kalidindi SR (2009) Determination of an effective zero-point and extraction of indentation stress–strain curves without the continuous stiffness measurement signal. Scr Mater 60(6):439–442

  91. 91.

    Hofmann DC et al (2014) Developing gradient metal alloys through radial deposition additive manufacturing. Sci Rep 4:5357

  92. 92.

    Zhang Y et al (2008) Characterization of laser powder deposited Ti–TiC composites and functional gradient materials. J Mater Process Technol 206(1):438–444

  93. 93.

    Bobbio LD et al (2017) Additive manufacturing of a functionally graded material from Ti–6Al–4 V to Invar: experimental characterization and thermodynamic calculations. Acta Mater 127:133–142

  94. 94.

    Qian T-T et al (2014) Microstructure of TA2/TA15 graded structural material by laser additive manufacturing process. Trans Nonferrous Met Soc China 24(9):2729–2736

  95. 95.

    Gu DD et al (2012) Laser additive manufacturing of metallic components: materials, processes and mechanisms. Int Mater Rev 57(3):133–164

  96. 96.

    Zuback JS, Palmer TA, DebRoy T (2019) Additive manufacturing of functionally graded transition joints between ferritic and austenitic alloys. J Alloy Compd 770:995–1003

  97. 97.

    Khosravani A, Cecen A, Kalidindi SR (2017) 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

  98. 98.

    Weaver JS et al (2016) On capturing the grain-scale elastic and plastic anisotropy of alpha-Ti with spherical nanoindentation and electron back-scattered diffraction. Acta Mater 117:23–34

  99. 99.

    Iskakov A et al (2018) 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

  100. 100.

    ASTM E8 / E8M-15a (2015) Standard Test Methods for Tension Testing of Metallic Materials. ASTM International, West Conshohocken, PA. https://www.astm.org

  101. 101.

    Standard Test Methods of Compression Testing of Metallic Materials at Room Temperature (2009). ASTM International

  102. 102.

    Standard Test Methods for Bend Testing of Metallic Flat Materials for Spring Applications Involving Static Loading (2013). ASTM International

  103. 103.

    Standard Test Method for Shear Modulus at Room Temperature. 2013, ASTM International

  104. 104.

    Kalidindi SR (2019) A Bayesian framework for materials knowledge systems. MRS Commun 9(2):518–531

  105. 105.

    Xue D et al (2016) Accelerated search for materials with targeted properties by adaptive design. Nat Commun 7:11241

  106. 106.

    Balachandran PV et al (2016) Adaptive strategies for materials design using uncertainties. Sci Rep 6:19660

  107. 107.

    Kiyohara S et al (2016) Acceleration of stable interface structure searching using a kriging approach. Jpn J Appl Phys 55(4):045502

  108. 108.

    Seko A et al (2014) Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single-and binary-component solids. Phys Rev B 89(5):054303

  109. 109.

    Seko A et al (2015) Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization. Phys Rev Lett 115(20):205901

  110. 110.

    Wigley PB et al (2016) Fast machine-learning online optimization of ultra-cold-atom experiments. Sci Rep 6:25890

  111. 111.

    Ueno T et al (2016) COMBO: an efficient Bayesian optimization library for materials science. Mater Discov 4:18–21

Download references

Acknowledgements

The authors acknowledge support for this work from NIST 70NANB18H039 (Program Manager: Dr. James Warren). MATIN platform was developed with support from GT’s IMAT and Office of Executive Vice-President for Research.

Author information

Correspondence to Surya R. Kalidindi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kalidindi, S.R., Khosravani, A., Yucel, B. et al. Data Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN. Integr Mater Manuf Innov 8, 441–454 (2019). https://doi.org/10.1007/s40192-019-00156-1

Download citation

Keywords

  • Data infrastructure
  • Materials innovation
  • Materials discovery
  • Materials informatics
  • Cyberinfrastructure
  • MGI