Skip to main content
Log in

Volunteer computing for computational materials design

  • Published:
Lobachevskii Journal of Mathematics Aims and scope Submit manuscript

Abstract

The problem of crystal structure prediction is very old and does, in fact, constitute the central problem of theoretical crystal chemistry. In this paper, we discuss the popular USPEX evolutionary algorithm for crystal structure prediction. Here we present the distributed computing implementation of USPEX based on a popular BOINC volunteer computing platform, and discuss experimental results and project performance.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Modern Methods of Crystal Structure Prediction, Ed. by A. R. Oganov (Wiley-VCH, Berlin, 2010).

  2. A. R. Oganov and C. W. Glass, “Crystal structure prediction using ab initio evolutionary techniques: principles and applications,” J. Chem. Phys. 124, 244704 (2006).

    Article  Google Scholar 

  3. W. W. Zhang, A. R. Oganov, A. F. Goncharov, Q. Zhu, S. E. Boulfelfel, A. O. Lyakhov, E. Stavrou, M. Somayazulu, V. B. Prakapenka, and Z. Konopkova, “Unexpected stoichiometries of stable sodium chlorides,” Science 342, 1502–1505 (2013).

    Article  Google Scholar 

  4. N. L. Matsko, E. V. Tikhonov, V. S. Baturin, S. V. Lepeshkin, and A. R. Oganov, “The impact of electron correlations on the energetics and stability of silicon nanoclusters,” J. Chem. Phys. 145, 074313 (2016).

    Article  Google Scholar 

  5. V. Sharma, C. Wang, R. G. Lorenzini, R. Ma, Q. Zhu, D. W. Sinkovits, G. Pilania, A. R. Oganov, S. Kumar, G. A. Sotzing, S. A. Boggs, and R. Ramprasad, “Rational design of all organic polymer dielectrics,” Nat. Commun. 5, 4845 (2014).

    Article  Google Scholar 

  6. D. P. Anderson, “Boinc: A system for public-resource computing and storage,” in Grid Computing, Proceedings of the 5th IEEE/ACMInternational Workshop, Nov. 8, 2004 (IEEE, Washington, DC, 2004), pp. 4–10.

    Google Scholar 

  7. K. Simons, C. Kooperberg, E. Huang, and D. Baker, “Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions,” J. Mol. Biol. 268, 209–225 (1997).

    Article  Google Scholar 

  8. J. D. Gale and A. L. Rohl, “The general utility lattice program (GULP),” Mol. Simul. 29, 291–341 (2003).

    Article  MATH  Google Scholar 

  9. A. R. Oganov, A. O. Lyakhov, and M. Valle, “How evolutionary crystal structure prediction works and why,” Accounts Chem. Res. 44, 227–237 (2011).

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Khrapov.

Additional information

Submitted by L. N. Shchur

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khrapov, N., Roizen, V., Posypkin, M. et al. Volunteer computing for computational materials design. Lobachevskii J Math 38, 926–930 (2017). https://doi.org/10.1134/S1995080217050195

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1995080217050195

Keywords and phrases

Navigation