Letter | Published:

A hybrid computational–experimental approach for automated crystal structure solution

Nature Materials volume 12, pages 123127 (2013) | Download Citation


Crystal structure solution from diffraction experiments is one of the most fundamental tasks in materials science, chemistry, physics and geology. Unfortunately, numerous factors render this process labour intensive and error prone. Experimental conditions, such as high pressure1 or structural metastability2, often complicate characterization. Furthermore, many materials of great modern interest, such as batteries3 and hydrogen storage media4, contain light elements such as Li and H that only weakly scatter X-rays. Finally, structural refinements generally require significant human input and intuition, as they rely on good initial guesses for the target structure. To address these many challenges, we demonstrate a new hybrid approach, first-principles-assisted structure solution (FPASS), which combines experimental diffraction data, statistical symmetry information and first-principles-based algorithmic optimization to automatically solve crystal structures. We demonstrate the broad utility of FPASS to clarify four important crystal structure debates: the hydrogen storage candidates MgNH and NH3BH3; Li2O2, relevant to Li–air batteries; and high-pressure silane, SiH4.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    , , , & Superconductivity in hydrogen dominant materials: Silane. Science 319, 1506–1509 (2008).

  2. 2.

    , & Crystal structures and shape-memory behaviour of NiTi. Nature Mater. 2, 307–311 (2003).

  3. 3.

    et al. Experimental visualization of lithium diffusion in LixFePO4. Nature Mater. 7, 707–711 (2008).

  4. 4.

    , & Accurate structure of LiAlD4 studied by combined powder neutron and X-ray diffraction. J. Alloy Compd. 346, 184–189 (2002).

  5. 5.

    & How to determine structures when single crystals cannot be grown: Opportunities for structure determination of molecular materials using powder diffraction data. Chem. Soc. Rev. 33, 526–538 (2004).

  6. 6.

    , , & The application of a genetic algorithm for solving crystal structures from powder diffraction data. Chem. Phys. Lett. 280, 189–195 (1997).

  7. 7.

    , & Structure determination of a complex organic solid from X-ray powder diffraction data by a generalized Monte Carlo method: The crystal structure of red fluorescein. Angew. Chem. Int. Ed. 36, 770–772 (1997).

  8. 8.

    & FOX, ‘free objects for crystallography’: A modular approach to ab initio structure determination from powder diffraction. J. Appl. Crystallogr. 35, 734–743 (2002).

  9. 9.

    & Computer prediction of organic crystal structures using partial X-ray diffraction data. J. Am. Chem. Soc. 118, 7153–7157 (1996).

  10. 10.

    & A periodic genetic algorithm with real-space representation for crystal structure and polymorph prediction. Phys. Rev. B 73, 224104 (2006).

  11. 11.

    , & USPEX—Evolutionary crystal structure prediction. Comput. Phys. Commun. 175, 713–720 (2006).

  12. 12.

    & Prototype electrostatic ground state approach to predicting crystal structures of ionic compounds: Application to hydrogen storage materials. Phys. Rev. B 77, 104115 (2008).

  13. 13.

    & Ab initio random structure searching. J. Phys. Condens. Matter 23, 053201 (2011).

  14. 14.

    & Darstellung und Eigenschaften von Magnesiumamid und -imid. Z. Anorg. Allg. Chem. 370, 254–261 (1969).

  15. 15.

    et al. Magnesium imide: Synthesis and structure determination of an unconventional alkaline earth imide from decomposition of magnesium amide. Inorg. Chem. 50, 1116–1122 (2011).

  16. 16.

    & How to quantify energy landscapes of solids. J. Chem. Phys. 130, 104504 (2009).

  17. 17.

    & XTALOPT: An open-source evolutionary algorithm for crystal structure prediction. Comput. Phys. Commun. 182, 372–387 (2011).

  18. 18.

    , , , & Crystal structure of ammonia monohydrate phase II. J. Am. Chem. Soc. 131, 13508–13515 (2009).

  19. 19.

    , , , & Lithium–air battery: Promise and challenges. J. Phys. Chem. Lett. 1, 2193–2203 (2010).

  20. 20.

    , & Beiträge zur Kenntnis des Wasserstoffperoxyds und seiner Derivate, VII. Über die Kristallstruktur des Lithiumperoxyds, Li2O2. Chem. Ber-Recl. 86, 1429–1437 (1953).

  21. 21.

    Die Kristallstrukturen der Alkaliperoxyde. Z. Anorg. Allg. Chem. 291, 12–50 (1957).

  22. 22.

    & On the structure of lithium peroxide, Li2O2. Acta Crystallogr. B 61, 133–136 (2005).

  23. 23.

    et al. Structure of lithium peroxide. J. Phys. Chem. Lett. 2, 2483–2486 (2011).

  24. 24.

    Will we soon be fueling our automobiles with ammonia-borane? Angew. Chem. Int. Ed. 46, 8116–8118 (2007).

  25. 25.

    , , & High-pressure phase and transition phenomena in ammonia borane NH3BH3 from X-ray diffraction, Landau theory, and ab initio calculations. Phys. Rev. B 79, 214111 (2009).

  26. 26.

    et al. Crystal structure of the pressure-induced metallic phase of SiH4 from ab initio theory. Proc. Natl Acad. Sci. USA 105, 16454–16459 (2008).

  27. 27.

    , , , & Formation of transition metal hydrides at high pressures. Solid State Commun. 149, 1583–1586 (2009).

Download references


B.M. was primarily supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. The work was further supported by DOE under Grant No. DE-FG02-07ER46433. The authors also gratefully acknowledge support from a Laboratory Directed Research and Development programme at Sandia National Laboratories, in the form of a Grand Challenge project entitled Reimagining Liquid Transportation Fuels: Sunshine to Petrol. B.M. would like to thank G. B. González Avilés, J. Emery, V. Ozoliņš and Y. Zhang for helpful conversations. B.M. is also indebted to A.U. Adler for lending his expertise to the creation of Fig. 1.

Author information


  1. Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, USA

    • Bryce Meredig
    •  & C. Wolverton


  1. Search for Bryce Meredig in:

  2. Search for C. Wolverton in:


C.W. and B.M. jointly conceived the work and analysed all results. B.M. designed the FPASS algorithm and carried out predictions. B.M. led the manuscript writing, with input from C.W.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to C. Wolverton.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Supplementary Information

About this article

Publication history






Further reading