Abstract
The prediction of structure at the atomic level is one of the most fundamental challenges in condensed matter science. Here we survey the current status of the field and consider recent developments in methodology, paying particular attention to approaches for surveying energy landscapes. We illustrate the current state of the art in this field with topical applications to inorganic, especially microporous solids, and to molecular crystals; we also look at applications to nanoparticulate structures. Finally, we consider future directions and challenges in the field.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Maddox, J. Crystals from first principles. Nature 335, 201–201 (1988).
Parker, S. C. Prediction of mineral crystal structures. Solid State Ionics 8, 179–186 (1983).
Catlow, C. R. A., Thomas, J. M., Parker, S. C. & Jefferson, D. A. Simulating silicate structures and the structural chemistry of pyroxenoids. Nature 295, 658–662 (1982).
Ghosht, A., Sarkarf, A. K. & Basus, A. N. The breathing shell model calculation of the relative stability of structure of alkali halide crystals. J. Phys. C 8, 1332–1338 (1975).
Donnay, G., Donnay, J. D. H. & Takeda, H. Trioctahedral one-layer micas. II. Prediction of the structure from composition and cell dimensions. Acta Cryst. 17, 1374–1381 (1964).
Catlow, C. R. A. & Price, G. D. Computer modelling of solid-state inorganic materials. Nature 347, 243–248 (1990).
Catlow, C. R. A. et al. Computer modelling of inorganic materials. Annu. Rep. Prog. Chem. A 101, 513–547 (2005).
Lewis, G. V. & Catlow, C. R. A. Potential models for ionic oxides. J. Phys. C 18, 1149–1161 (1985).
Shannon, M. D., Casci, J. L., Cox, P. A. & Andrews, S. J. Structure of the two-dimensional medium-pore high-silica zeolite NU-87. Nature 353, 417–420 (1991).
Kirkpatrick, S., Gellat, J. C. D. & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671–680 (1983).
Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A. & Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953).
Pannetier, J., Bassas-Alsina, J., Rodriguez-Carvajal, J. & Caignaert, V. Prediction of crystal structures from crystal chemistry rules by simulated annealing. Nature 346, 343–345 (1990).
Schön, J. C. & Jansen, M. First step towards planning of syntheses in solid-state chemistry: Determination of promising structure candidates by global optimization. Angew. Chem. Int Ed. Engl. 35, 1287–1304 (1996).
Schön, J. C. & Jansen, M. Determination, prediction, and understanding of structures, using the energy landscapes of chemical systems. Z. Kristallogr. 216, 307–325 (2001).
Mellot-Draznieks, C., Newsam, J. M., Gorman, A. M., Freeman, C. M. & Férey, G. De novo prediction of inorganic structures developed through automated assembly of secondary building units (AASBU method). Angew. Chem. Int. Ed. 39, 2270–2275 (2000).
Mellot-Draznieks, C. et al. Computational design and prediction of interesting not-yet-synthesized structures of inorganic materials by using building unit concepts. Chem. Eur. J. 8, 4102–4113 (2002).
Mellot-Draznieks, C., Girard, S. & Férey, G. R. Novel inorganic frameworks constructed from double-four-ring (D4R) units: Computational design, structures, and lattice energies of silicate, aluminophosphate, and gallophosphate candidates. J. Am. Chem. Soc. 124, 15326–15335 (2002).
Mellot-Draznieks, C., Dutour, J. & Férey, G. R. Hybrid organic–inorganic frameworks: Routes for computational design and structure prediction. Angew. Chem. Int. Ed. 43, 6290–6296 (2004).
Wales, D. J. & Scheraga, H. A. Review: Chemistry. Global optimization of clusters, crystals, and biomolecules. Science 285, 1368–1372 (1999).
Wales, D. J. & Doyle, J. P. K. Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J. Phys. Chem. A 101, 5111–5116 (1997).
Hamad, S., Catlow, C. R. A., Woodley, S. M., Lago, S. & Mejías, J. A. Structure and stability of small TiO2 nanoparticles. J. Phys. Chem. B 109, 15741–15748 (2005).
Coley, D. A. An Introduction to Genetic Algorithms for Scientists and Engineers (World Scientific, 1999).
Lloyd, L. D., Johnston, R. L. & Salhi, S. Strategies for increasing the efficiency of a genetic algorithm for the structural optimization of nanoalloy clusters. J. Comp. Chem. 26, 1069–1078 (2005).
Hartke, B. in Applications of Evolutionary Computation in Chemistry, 33–53 (Springer, 2004).
Johnston, R. L. Evolving better nanoparticles: Genetic algorithms for optimising cluster geometries. Dalton Trans. 22, 4193–4207 (2003).
Woodley, S. M. Engineering microporous architectures: Combining an evolutionary algorithm with predefined exclusion zones. Phys. Chem. Chem. Phys. 9, 1070–1077 (2006).
Abraham, N. L. & Probert, M. I. J. A periodic genetic algorithm with real-space representation for crystal structure and polymorph prediction. Phys. Rev. B 73, 224104 (2006).
Woodley, S. M. in Applications of Evolutionary Computation in Chemistry, 95–132 (Springer, 2004).
Harris, K. D. M., Johnston, R. L. & Habershon, S. in Applications of Evolutionary Computation in Chemistry, 55–94 (Springer, 2004).
Turner, G. W., Tedesco, E., Harris, K. D. M., Johnston, R. L. & Kariuki, B. M. Implementation of Lamarckian concepts in a genetic algorithm for structure solution from powder diffraction data. Chem. Phys. Lett. 321, 183–190 (2000).
Roberts, C., Johnston, R. L. & Wilson, N. T. A genetic algorithm for the structural optimization of Morse clusters. Theor. Chem. Acc. 104, 123–130 (2000).
Oganov, A. R. & Glass, C. W. Crystal structure prediction using ab initio evolutionary techniques: Principles and applications. J. Chem. Phys. 124, 244704 (2006).
Woodley, S. M. & Catlow, C. R. A. Structure prediction of titania phases: Implementation of Darwinian versus Lamarckian concepts in an evolutionary algorithm. Comp. Mater. Sci. (in the press).
Pickard, C. J. & Needs, R. J. When is H2O not water? J. Chem. Phys. 127, 244503 (2007).
Wells, A. F. The geometrical basis of crystal chemistry. 1–4. Acta Crystallogr. 7, 535–554; 842–853 (1954).
Smith, J. V. Enumeration of 4-connected 3-dimensional nets and classification of framework silicates. 1. Perpendicular linkage from simple hexagonal net. Am. Mineral. 62, 703–709 (1977).
Smith, J. V. Enumeration of 4-connected 3-dimensional nets and classification of framework silicates. 2. Perpendicular and near-perpendicular linkages from 4.82, 3.122 and 4.6.12 nets. Am. Mineral. 63, 960–969 (1978).
Smith, J. V. Enumeration of 4-connected 3-dimensional nets and classification of framework silicates. 3. Combination of helix, and zigzag, crankshaft and saw chains with simple 2d-nets. Am. Mineral. 64, 551–562 (1979).
O'Keefe, M. & Hyde, B. G. Crystal Structures I. Patterns and Symmetry (Mineral. Soc. Am., 1996).
Treacy, M. M. J., Randall, K. H., Rao, S., Perry, J. A. & Chadi, D. J. Enumeration of periodic tetrahedral frameworks. Z. Kristallogr. 212, 768–791 (1997).
Treacy, M. M. J., Rivin, I., Balkovsky, E., Randall, K. H. & Foster, M. D. Enumeration of periodic tetrahedral frameworks. II. Polynodal graphs. Micropor. Mesopor. Mater. 74, 121–132 (2004).
Foster, M. D. et al. Chemically feasible hypothetical crystalline networks. Nature Mater. 3, 234–238 (2004).
Dress, A. W. M., Huson, D. H. & Molnar, E. The classification of face-transitive periodic 3-dimensional tilings. Acta Crystallogr. A 49, 806–817 (1993).
Delgado, O., Huson, D. & Zamorzaeva, E. The classification of 2-isohedral tilings of the plane. Geometriae Dedicata 42, 43–117 (1992).
Friedrichs, O. D., Dress, A. W. M., Huson, D. H., Klinowski, J. & Mackay, A. L. Systematic enumeration of crystalline networks. Nature 400, 644–647 (1999).
O'Keeffe, M. Three-periodic nets and tilings: regular and related infinite polyhedra. Acta Crystallogr. A 64, 425–429 (2008).
Winkler, B., Pickard, C. J., Milman, V. & Thimm, G. Systematic prediction of crystal structures. Chem. Phys. Lett. 337, 36–42 (2001).
Le Bail, A. Inorganic structure prediction with GRINSP. J. Appl. Cryst. 38, 389–395 (2005).
Tajima, N., Tsuzuki, S., Tanabe, K., Aoki, K. & Hirano, T. First principles prediction of crystal structures of CO2 . Electron. J. Theor. Chem. 2, 139–148 (1997).
Arikawa, T., Tajima, N., Tsuzuki, S., Tanabe, K. & Hirano, T. A possible crystal-structure of 1,2-dimethoxyethane—prediction based on a lattice variable molecular-dynamics. Theochem: J. Mol. Struct. 339, 115–124 (1995).
Hirano, T., Tsuzuki, S., Tanabe, K. & Tajima, N. Totally ab initio prediction of the structures of CO2 molecular crystal. Chem. Lett. 12, 1073–1074 (1995).
Chaka, A. M., Zaniewski, R., Youngs, W., Tessier, C. & Klopman, G. Predicting the crystal structure of organic molecular materials. Acta Crystallogr. B 52, 165–183 (1996).
Ammon, H. L., Du, Z. Y., Holden, J. R. & Paquette, L. A. Acta Crystallogr. B 50, 216–220 (1994).
Van Eijck, B. P. & Kroon, J. Upack program package for crystal structure prediction: force fields and crystal structure generation for small carbohydrate molecules. J. Comput. Chem. 20, 799–812 (1999).
Holden, J. R., Du, Z. Y. & Ammon, H. L. Prediction of possible crystal-structures for C-containing, H-containing, N-containing, O-containing and F-containing organic-compounds. J. Comput. Chem. 14, 422–437 (1993).
Aakeroy, C. B., Nieuwenhuyzen, M. & Price, S. L. Three polymorphs of 2-amino-5-nitropyrimidine: Experimental structures and theoretical predictions. J. Am. Chem. Soc. 120, 8986–8993 (1998).
Beyer, T. & Price, S. L. Dimer or catemer? Low-energy crystal packings for small carboxylic acids. J. Phys. Chem. B 104, 2647–2655 (2000).
Price, S. L. & Wibley, K. S. Predictions of crystal packings for uracil, 6-azauracil, and allopurinol: The interplay between hydrogen bonding and close packing. J. Phys. Chem. A 101, 2198–2206 (1997).
Beyer, T., Day, G. M. & Price, S. L. The prediction, morphology, and mechanical properties of the polymorphs of paracetamol. J. Am. Chem. Soc. 123, 5086–5094 (2001).
Gdanitz, R. J. Prediction of molecular-crystal structures by Monte-Carlo simulated annealing without reference to diffraction data. Chem. Phys. Lett. 190, 391–396 (1992).
Price, S. L. From crystal structure prediction to polymorph prediction: interpreting the crystal energy landscape. Phys. Chem. Chem. Phys. 10, 1996–2009 (2008).
Dunitz, J. D. & Gavezzotti, A. Molecular recognition in organic crystals: Directed intermolecular bonds or nonlocalized bonding? Angew. Chem. Int. Ed. 44, 1766–1787 (2005).
Desiraju, G. R. Crystal engineering: A holistic view. Angew. Chem. Int. Ed. 46, 8342–8356 (2007).
Raiteri, P., Martoňák, R. & Parrinello, M. Exploring polymorphism: The case of benzene. Angew. Chem. Int. Ed. 44, 3769–3773 (2005).
Curtarolo, S., Morgan, D., Persson, K., Rodgers, J. & Ceder, G. Predicting crystal structures with data mining of quantum calculations. Phys. Rev. Lett. 91, 135503 (2003).
Fischer, C. C., Tibbetts, K. J., Morgan, D. & Ceder, G. Predicting crystal structure by merging data mining with quantum mechanics. Nature Mater. 5, 641–646 (2006).
Hofmann, D. W. M. & Apostolakis, J. Crystal structure prediction by data mining. J. Mol. Struct. 647, 17–39 (2003).
Schön, J. C., Čančarević, Ž. P., Hannermann, A. & Jansen, M. Free enthalpy landscape of SrO. J. Chem. Phys. 128, 194712 (2008).
Martoňák, R., Laio, A. & Parrinello, M. Predicting crystal structures: The Parrinello–Rahman method revisited. Phys. Rev. Lett. 90, 75503 (2003).
Schön, J. C., Pentin, I. V. & Jansen, M. Ab initio computation of the low-temperature phase diagrams of the alkali metal iodide-bromides: MBrxI1−x (0 ≤ x ≤ 1), where M = Li, Na, K, Rb, or Cs. J. Phys. Chem. B 111, 3943–3952 (2007).
Ceriani, C. et al. Molecular dynamics simulation of reconstructive phase transitions on an anhydrous zeolite. Phys. Rev. B 70, 113403 (2004).
Brown, I. D. Computer Modelling in Inorganic Crystallography Ch. 2 (ed. Catlow, C. R. A.) (Academic, 1994).
Lacorre, P., Pannetier, J., Hoppe, R., Averdunk, F. & Ferey, G. Crystal and magnetic-structures of LiCoF4—the 1st compound with a dirutile structure. J. Solid State Chem. 79, 1–11 (1989).
Freeman, C. M. & Catlow, C. R. A. Structure predictions in inorganic solids. J. Chem. Soc. Chem. Commun. 89–91 (1992).
Freeman, C. M., Newman, J. M., Levine, S. M. & Catlow, C. R. A. Inorganic crystal-structure prediction using simplified potentials and experimental unit cells—application to the polymorphs of titanium-dioxide. J. Mater. Chem. 3, 531–535 (1993).
Woodley, S. M., Battle, P. D., Gale, J. D. & Catlow, C. R. A. The prediction of inorganic crystal structures using a genetic algorithm and energy minimisation. Phys. Chem. Chem. Phys. 1, 2535–2542 (1999).
Reinaudi, L., Carbonio, R. E. & Leiva, E. P. M. Inclusion of symmetry for the enhanced determination of crystalline structures from powder diffraction data using simulated annealing. J. Chem. Soc. Chem. Commun. 255–256 (1998).
Reinaudi, L., Leiva, E. P. M. & Carbonia, R. E. Simulated annealing prediction of the crystal structure of ternary inorganic compounds using symmetry restrictions. J. Chem. Soc., Dalton Trans. 23, 4258–4262 (2000).
Bush, T. S., Catlow, C. R. A. & Battle, P. D. Evolutionary programming techniques for predicting inorganic crystal-structures. J. Mater. Chem. 5, 1269–1272 (1995).
Doll, K., Schön, J. C. & Jansen, M. Global exploration of the energy landscape of solids on the ab initio level. Phys. Chem. Chem. Phys. 9, 6128–6133 (2007).
Schön, J. C. & Jansen, M. Determination of candidate structures for Lennard-Jones-crystals through cell optimization. Ber. Bunsenges Phys. Chem. 98, 1541–1544 (1994).
Jansen, M. & Schön, J. C. Structure candidates for the alkali metal nitrides. Z. Anorg. Allg. Chem. 624, 533–540 (1998).
Putz, H., Schön, J. C. & Jansen, M. Investigation of the energy landscape of Mg2OF2 . Comput. Mater. Sci. 11, 309–322 (1998).
Wevers, M. A. C., Schön, J. C. & Jansen, M. Determination of structure candidates of simple crystalline AB2 systems. J. Solid State Chem. 136, 233–246 (1998).
Schön, J. C., Wevers, M. A. C. & Jansen, M. Prediction of high pressure phases in the systems Li3N, Na3N, (Li,Na)3N, Li2S and Na2S. J. Mater. Chem. 11, 69–77 (2001).
Ciobanu, C. V., Chuang, F. C. & Lytle, D. E. On the structure of the Si(103) surface. Appl. Phys. Lett. 91, 171909 (2007).
Briggs, R. M. & Ciobanu, C. V. Evolutionary approach for finding the atomic structure of steps on stable crystal surfaces. Phys. Rev. B 75, 195415 (2007).
Kasuya, A. et al. Ultra-stable nanoparticles of CdSe revealed from mass spectrometry. Nature Mater. 3, 99–102 (2004).
Hamad, S., Cristol, S. & Catlow, C. R. A. Simulation of the embryonic stage of ZnS formation from aqueous solution. J. Am. Chem. Soc. 127, 2580–2590 (2005).
Wakisaka, A. Nucleation in alkali metal chloride solution observed at the cluster level. Faraday Discuss. 136, 299–308 (2007).
Burnin, A. & Belbruno, J. J. ZnnSm+ cluster production by laser ablation. Chem. Phys. Lett. 362, 341–348 (2002).
Whetten, R. L. Alkali-halide nanocrystals. Acc. Chem. Res. 26, 49–56 (1993).
Hamad, S., Catlow, C. R. A., Spano, E., Matxain, J. M. & Ugalde, J. M. Structure and properties of ZnS nanoclusters. J. Phys. Chem. B 109, 2703–2709 (2005).
Al-Sunaidi, A. A., Sokol, A. A., Catlow, C. R. A. & Woodley, S. M. Structures of zinc oxide nanoclusters: As found by evolutionary algorithm techniques. J. Phys. Chem. C (in the press).
Hamad, S. & Catlow, C. R A. Computational study of the relative stabilities of ZnS clusters, for sizes between 1 and 4 nm. J. Cryst. Growth 294, 2–8 (2006).
Michaelian, K. Evolving few-ion clusters of Na and Cl. Am. J. Phys. 66, 231–240 (1998).
Wootton, A. & Harrowell, P. Inorganic nanotubes stabilized by ion size asymmetry: Energy calculations for AgI clusters. J. Phys. Chem. B 108, 8412–8418 (2004).
Roberts, C. & Johnston, R. L. Investigation of the structures of MgO clusters using a genetic algorithm. Phys. Chem. Chem. Phys. 3, 5024–5034 (2001).
Woodley, S. M., Sokol, A. A. & Catlow, C. R. A. Structure prediction of inorganic nanoclusters with a predefined architecture using a genetic algorithm. Z. Anorg. Allg. Chem. 630, 2343–2353 (2004).
Flikkema, E. & Bromley, S. T. Dedicated global optimization search for ground state silica nanoclusters: (SiO2)N (N = 6–12). J. Phys. Chem. B 108, 9638–9645 (2004).
Shevlin, S. A. et al. Structure, optical properties and defects in nitride (III–V) nanoscale cage clusters. Phys. Chem. Chem. Phys. 10, 1944–1959 (2008).
Michaelian, K., Rendón, N. & Garzón, I. L. Structure and energetics of Ni, Ag, and Au nanoclusters. Phys. Rev. B 60, 20003–2010 (1999).
Ferrando, R., Fortunelli, A. & Johnston, R. L. Searching for the optimum structures of alloy nanoclusters. Phys. Chem. Chem. Phys. 10, 640–649 (2008).
Paz-Borbon, L. O., Johnston, R. L., Barcaro, G. & Fortunelli, A. Structural motifs, mixing, and segregation effects in 38-atom binary clusters. J. Chem. Phys. 128, 134517 (2008).
Deem, M. W. & Newsam, J. M. Determination of 4-connected framework crystal-structures by simulated annealing. Nature 342, 260–262 (1989).
Deem, N. W. & Newsam, J. M. Framework crystal-structure solution by simulated annealing: test application to known zeolite structures. J. Am. Chem. Soc. 114, 7189–7198 (1992).
Falcioni, M. & Deem, M. W. A biased Monte Carlo scheme for zeolite structure solution. J. Chem. Phys. 110, 1754–1766 (1999).
Akporiaye, D. E. et al. UiO-7: A new aluminophosphate phase solved by simulated annealing and high-resolution powder diffraction. J. Phys. Chem. 100, 16641–16646 (1996).
Boisen, M. B., Gibbs, G. V. & Bukowinski, M. S. T. Framework silica structures generated using simulated annealing with a potential-energy function-based on an H6Si2O7 molecule. Phys. Chem. Miner. 21, 269–284 (1994).
Teter, D. M., Gibbs, G. V., Boisen, M. B., Allan, D. C. & Teter, M. P. First-principles study of several hypothetical silica framework structures. Phys. Rev. B 52, 8064–8073 (1995).
Boisen, M. B., Gibbs, G. V., O'Keeffe, M. & Bartelmehs, K. L. A generation of framework structures for the tectosilicates using a molecular-based potential energy function and simulated annealing strategies. Micropor. Mesopor. Mater. 29, 219–266 (1999).
Woodley, S. M., Catlow, C. R. A., Battle, P. D. & Gale, J. D. The prediction of close packed and porous inorganic crystal structures. Acta Cryst. A 58, C196 (2002).
Woodley, S. M. Prediction of inorganic crystal framework structures. Part II: using a genetic algorithm and a direct approach to exclusion zones. Phys. Chem. Chem. Phys. 6, 1823–1829 (2004).
Woodley, S. M., Battle, P. D., Gale, J. D. & Catlow, C. R. A. Prediction of inorganic crystal framework structures. Part I: Using a genetic algorithm and an indirect approach to exclusion zones. Phys. Chem. Chem. Phys. 6, 1815–1822 (2004).
Zwijnenburg, M. A., Cora, F. & Bell, R. G. Dramatic differences between the energy landscapes of SiO2 and SiS2 zeotype materials. J. Am. Chem. Soc. 129, 12588–12589 (2007).
Carrasco, J., Illas, F. & Bromley, S. T. Ultralow-density nanocage-based metal-oxide polymorphs. Phys. Rev. Lett. 99, 235502 (2007).
Lewis, D. W., Catlow, C. R. A., Thomas, J. M., Willock, D. J. & Hutchings, G. J. De novo design of structure-directing agents for the synthesis of microporous solids. Nature 382, 604–606 (1996).
Sankar, G. et al. Structure of templated microcrystalline DAF-5 (Co0.28Al0.72PO4C10H20N2) determined by synchrotron-based diffraction methods. Chem. Commun. 1, 117–118 (1998).
Hulme, A. T., Price, S. L. & Tocher, D. A. A new polymorph of 5-fluorouracil found following computational crystal structure predictions. J. Am. Chem. Soc. 127, 1116–1117 (2005).
Hamad, S., Moon, C., Catlow, C. R. A., Hulme, A. T. & Price, S. L. Kinetic insights into the role of the solvent in the polymorphism of 5-fluorouracil from molecular dynamics simulations. J. Phys. Chem. B 110, 3323–3329 (2006).
Lommerse, J. P. M. et al. A test of crystal structure prediction of small organic molecules. Acta Crystallogr. B 56, 697–714 (2000).
Neumann, M. A., Leusen, F. J. J. & Kendrick, J. A major advance in crystal structure prediction. Angew. Chem. Int. Ed. 47, 2427–2430 (2008).
Moult, J. A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr. Opin. Struct. Biol. 15, 285–289 (2005).
Petrey, D. & Honig, B. Protein structure prediction: Inroads to biology. Mol. Cell 20, 811–819 (2005).
Floudas, C. A., Fung, H. K., McAllister, S. R., Monnigmann, M. & Rajgaria, R. Advances in protein structure prediction and de novo protein design: A review. Chem. Eng. Sci. 61, 966–988 (2006).
Zhang, Y. Progress and challenges in protein structure prediction. Curr. Opin. Struct. Biol. 18, 342–348 (2008).
Jansen, M. A concept for synthesis planning in solid-state chemistry. Angew. Chem. Int. Ed. 41, 3746–3766 (2002).
Jansen, M. in Turning Points in Solid-State, Materials and Surface Science (eds Harris, K. M. & Edwards, P.) 22–50 (Royal Society of Chemistry, 2008).
Cancarevic, Z. P., Schön, J. C. & Jansen, M. Stability of alkali metal halide polymorphs as a function of pressure. Chem. Asian J. 3, 561–572 (2008).
Liebold-Ribeiro, Y., Fischer, D. & Jansen, M. Experimental substantiation of the 'Energy landscape concept' for solids: Synthesis of a new modification of LiBr. Angew. Chem. Int. Ed. 47, 4428–4431 (2008).
Acknowledgements
We thank R. G. Bell, S. Hamad, M. Jansen, S. L. Price, J. C. Schön, A. A. Sokol and J. M. Thomas for discussions, and the EPSRC for financial support via the Portfolio Partnership grant EP/D504872.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Woodley, S., Catlow, R. Crystal structure prediction from first principles. Nature Mater 7, 937–946 (2008). https://doi.org/10.1038/nmat2321
Issue Date:
DOI: https://doi.org/10.1038/nmat2321
This article is cited by
-
Comparative study of crystal structure prediction approaches based on a graph network and an optimization algorithm
Science China Materials (2024)
-
Optimality guarantees for crystal structure prediction
Nature (2023)
-
A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery
npj Computational Materials (2023)
-
Ammonia dimer: extremely fluxional but still hydrogen bonded
Nature Communications (2022)
-
Highly accurate machine learning prediction of crystal point groups for ternary materials from chemical formula
Scientific Reports (2022)