Skip to main content
SpringerLink
Log in

Online Tools for Uncertainty Quantification in nanoHUB

  • Multiscale Computational Strategies for Heterogeneous Materials with Defects: Coupling Modeling with Experiments and Uncertainty Quantification
  • Published:
JOM Aims and scope Submit manuscript

Abstract

nanoHUB is a cyberinfrastructure for cloud computing, where simulation tool developers can make their products available to their community and users can run fully interactive simulations from a standard web-browser. We report recent cyberinfrastructure developments and workflows that enable calibration and automatic uncertainty propagation for the 400+ nanoHUB tools that use the rapid application infrastructure (Rappture). A Jupyter notebook enables users to upload training datasets, connect these data with the desired tool outputs and specify the calibration variables. The tool uses the Dakota software package that enables various calibration methods. We demonstrate its use via Bayesian calibration of an interatomic potential for molecular dynamics. We also extended Rappture to include automatic uncertainty propagation in deterministic tools. Real-valued tool inputs can be specified as a distribution, and nanoHUB uses collocation to perform multiple simulations spanning the input ranges. Simulation results are post-processed to create surrogate models, perform sensitivity analysis and uncertainty propagation.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. E.B. Tadmor and R.E. Miller, Modeling Materials: Continuum, Atomistic and Multiscale Techniques (Cambridge: Cambridge University Press, 2011).

    Book  MATH  Google Scholar 

  2. D.J. Luscher, D.L. McDowell, and C.A. Bronkhorst, Int. J. Plast. 26, 1248 (2010).

    Article  Google Scholar 

  3. A.M. Cuitiño, L. Stainier, G. Wang, A. Strachan, T. Çağin, W.A. Goddard, M. Ortiz, and J. Comput-Aid, Mater. Des. 8, 127 (2001).

    Google Scholar 

  4. M. Koslowski and A. Strachan, Reliab. Eng. Syst. Saf. 96, 1161 (2011).

    Article  Google Scholar 

  5. M. Hunt, B. Haley, M. McLennan, M. Koslowski, J. Murthy, and A. Strachan, Comput. Phys. Commun. 194, 97 (2015).

    Article  Google Scholar 

  6. S. Wojtkiewicz, M. Eldred, R. Field, Jr, A. Urbina, and J. Red-Horse, in 19th AIAA Applied Aerodynamics Conference (2001), p. 1455.

  7. B.M. Adams, W. Bohnhoff, K. Dalbey, J. Eddy, M. Eldred, D. Gay, K. Haskell, P.D. Hough, and L. P. Swiler, Sandia National Laboratories, Tech. Rep. SAND2010-2183 (2009).

  8. https://nanohub.org. Accessed 1 Mar 2019.

  9. A. Strachan, G. Klimeck, and M. Lundstrom, Comput. Sci. Eng. 12, 12 (2010).

    Article  Google Scholar 

  10. https://nanohub.org/usage. Accessed 1 Mar 2019.

  11. https://nanohub.org/resources/new. Accessed 1 Mar 2019.

  12. S. Plimpton, J. Comput. Phys. 117, 1 (1995).

    Article  Google Scholar 

  13. D. Gaston, C. Newman, G. Hansen, and D. Lebrun-Grandie, Nucl. Eng. Des. 239, 1768 (2009).

    Article  Google Scholar 

  14. P. Giannozzi, S. Baroni, N. Bonini, M. Calandra, R. Car, C. Cavazzoni, D. Ceresoli, G.L. Chiarotti, M. Cococcioni, and I. Dabo, J. Phys. Condens. Matter 21, 395502 (2009).

    Article  Google Scholar 

  15. X. Gonze, J.-M. Beuken, R. Caracas, F. Detraux, M. Fuchs, G.-M. Rignanese, L. Sindic, M. Verstraete, G. Zerah, and F. Jollet, Comput. Mater. Sci. 25, 478 (2002).

    Article  Google Scholar 

  16. M. McLennan and R. Kennell, Comput. Sci. Eng. 12, 48 (2010).

    Article  Google Scholar 

  17. M. McLennan, Rappture Bootcamp: Building and Deploying Tools (2012), https://nanohub.org/resources/14671.

  18. T. Kluyver, et al., in Positioning and Power in Academic Publishing: Players, Agents and Agendas. Proceedings of the 20th International Conference on Electronic Publishing (2016), pp. 87–90.

  19. https://jupyter.org. Accessed 1 Mar 2019.

  20. L. Bergamasco, M. Fasano, E. Chiavazzo, P. Asinari, A. Cardellini, and M. Morciano (2017). https://nanohub.org/resources/tcnt, https://doi.org/10.21981/d3rv0d32v

  21. J.C.V. Gastelum, A. Strachan, and S. Desai (2019). https://nanohub.org/resources/mseml, https://doi.org/10.21981/9qjn-7n65.

  22. M. Hunt, B.P. Haley, J. Ebinger, and A. Strachan (2015). https://nanohub.org/resources/bayes, https://doi.org/10.21981/d3h12v838.

  23. S. Langer, R. Edwin García, and A. Reid (2016). https://nanohub.org/resources/oof2, https://doi.org/10.21981/d31z41t94.

  24. https://nanohub.org/infrastructure/rappture/wiki/rappture_xml_elements. Accessed 1 Mar 2019.

  25. A. Strachan, T. Çağin, and W.A. Goddard III, Phys. Rev. B 60, 15084 (1999).

    Article  Google Scholar 

  26. A.P. Thompson, L.P. Swiler, C.R. Trott, S.M. Foiles, and G.J. Tucker, J. Comput. Phys. 285, 316 (2015).

    Article  MathSciNet  Google Scholar 

  27. W.J. Szlachta, A.P. Bartók, and G. Csányi, Phys. Rev. B 90, 104108 (2014).

    Article  Google Scholar 

  28. S. Desai and A. Strachan (2019). https://nanohub.org/resources/dakotacalibrate, https://doi.org/10.21981/4q55-ft75.

  29. https://github.com/hubzero/hublib. Accessed 1 Mar 2019.

  30. M.C. Kennedy and A. O’Hagan, J. R. Stat. Soc. B 63, 425 (2001).

    Article  Google Scholar 

  31. J. Snoek, H. Larochelle, and R.P. Adams, in Advances in Neural Information Processing Systems (2012), pp. 2951–2959.

  32. S.L. Frederiksen, K.W. Jacobsen, K.S. Brown, and J.P. Sethna, Phys. Rev. Lett. 93, 165501 (2004).

    Article  Google Scholar 

  33. S. Longbottom and P. Brommer, ArXiv Preprint arXiv:1812.00863 (2018).

  34. M. Wen, J. Li, P. Brommer, R.S. Elliott, J.P. Sethna, and E.B. Tadmor, Model. Simul. Mater. Sci. Eng. 25, 014001 (2016).

    Article  Google Scholar 

  35. A. Sutton and J. Chen, Philos. Mag. Lett. 61, 139 (1990).

    Article  Google Scholar 

  36. P. Haas, F. Tran, and P. Blaha, Phys. Rev. B 79, 085104 (2009).

    Article  Google Scholar 

  37. P. Philipsen and E. Baerends, Phys. Rev. B 54, 5326 (1996).

    Article  Google Scholar 

  38. Y. Mishin, M. Mehl, D. Papaconstantopoulos, A. Voter, and J. Kress, Phys. Rev. B 63, 224106 (2001).

    Article  Google Scholar 

  39. H. Polatoglou, M. Methfessel, and M. Scheffler, Phys. Rev. B 48, 1877 (1993).

    Article  Google Scholar 

  40. K. Choudhary, G. Cheon, E. Reed, and F. Tavazza, Phys. Rev. B 98, 014107 (2018).

    Article  Google Scholar 

  41. S. Desai and A. Strachan (2019). https://nanohub.org/resources/calibdriver, https://doi.org/10.21981/9xq9-k306.

  42. A. Strachan, S. Mahadevan, V. Hombal, and L. Sun, Model. Simul. Mater. Sci. Eng. 21, 065009 (2013).

    Article  Google Scholar 

  43. D. Xiu and G.E. Karniadakis, SIAM J. Sci. Comput. 24, 619 (2002).

    Article  MathSciNet  Google Scholar 

  44. M.D. McKay, R.J. Beckman, and W.J. Conover, Technometrics 42, 55 (2000).

    Article  Google Scholar 

  45. M.D. Morris, Technometrics 33, 161 (1991).

    Article  Google Scholar 

  46. F. Campolongo, J. Cariboni, and A. Saltelli, Environ. Model. Softw. 22, 1509 (2007).

    Article  Google Scholar 

  47. A. Ritchey (2016) Composite laminate analysis. https://nanohub.org/resources/complam, https://doi.org/10.21981/d33x83m8k.

  48. G. Javier, U. Kamran, D.M. Guzman, A. Strachan, and P. Liao (2017) DFT material properties simulator. https://nanohub.org/resources/dftmatprop, https://doi.org/10.21981/d30g3h12q.

  49. A.E. Mattsson, P.A. Schultz, M.P. Desjarlais, T.R. Mattsson, and K. Leung, Model. Simul. Mater. Sci. Eng. 13, R1 (2004).

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the US National Science Foundation EEC-1227110, Network for Computational Nanotechnology.

Author information

Authors and Affiliations

  1. School of Materials Engineering, Birck Nanotechnology Center, Purdue University, West Lafayette, IN, 47907, USA

    Saaketh Desai, Martin Hunt & Alejandro Strachan

Authors
  1. Saaketh Desai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Hunt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alejandro Strachan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alejandro Strachan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Desai, S., Hunt, M. & Strachan, A. Online Tools for Uncertainty Quantification in nanoHUB. JOM 71, 2635–2645 (2019). https://doi.org/10.1007/s11837-019-03534-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11837-019-03534-4

Search

Navigation