Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Computational predictions of energy materials using density functional theory

Abstract

In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Procedure to screen for materials properties using density functional theory calculations.
Figure 2: Computational design of high-rate-capability Li-ion battery materials.
Figure 3: Prediction of superconductivity in FeB4.
Figure 4: Screening of copolymers for organic photovoltaics.
Figure 5: LiZnSb: a candidate thermoelectric material suggested by computation.

References

  1. 1

    Dirac, P. A. M. Quantum mechanics of many-electron systems. Proc. R. Soc. Lond. A 123, 714–733 (1929).

    CAS  Article  Google Scholar 

  2. 2

    Schrödinger, E. An undulatory theory of the mechanics of atoms and molecules. Phys. Rev. 22, 1049 (1926).

    Article  Google Scholar 

  3. 3

    Foulkes, W., Mitas, L., Needs, R. & Rajagopal, G. Quantum Monte Carlo simulations of solids. Rev. Mod. Phys. 73, 33–83 (2001).

    CAS  Article  Google Scholar 

  4. 4

    Hohenberg, P. & Kohn, W. Inhomogeneous electron gas. Phys. Rev. 136, B864–B871 (1964). This study established the theoretical basis of density functional theory.

    Article  Google Scholar 

  5. 5

    Kohn, W. & Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133 (1965). This paper described the Kohn–Sham theorems that paved the way for practical implementations of density functional theory.

    Article  Google Scholar 

  6. 6

    van Noorden, R., Maher, B. & Nuzzo, R. The top 100 papers. Nature 514, 550–553 (2014).

    CAS  Article  Google Scholar 

  7. 7

    Ceder, G. Predicting properties from scratch. Science 280, 1099–1100 (1998).

    CAS  Article  Google Scholar 

  8. 8

    Hafner, J., Wolverton, C. & Ceder, G. Towards computational materials design: the impact of density functional theory on materials research. MRS Bull. 31, 659–668 (2006).

    Article  Google Scholar 

  9. 9

    Hautier, G., Jain, A. & Ong, S. P. From the computer to the laboratory: materials discovery and design using first-principles calculations. J. Mater. Sci. 47, 7317–7340 (2012).

    CAS  Article  Google Scholar 

  10. 10

    Wilmer, C. E. et al. Large-scale screening of hypothetical metal–organic frameworks. Nat. Chem. 4, 83–89 (2012).

    CAS  Article  Google Scholar 

  11. 11

    Schmidt, J. E., Deem, M. W. & Davis, M. E. Synthesis of a specified, silica molecular sieve by using computationally predicted organic structure-directing agents. Angew. Chem. Int. Ed. Engl. 53, 8372–8374 (2014).

    CAS  Article  Google Scholar 

  12. 12

    Schmidt, J. E., Deem, M. W., Lew, C. & Davis, T. M. Computationally-guided synthesis of the 8-ring Zeolite AEI. Top. Catal. 58, 410–415 (2015).

    CAS  Article  Google Scholar 

  13. 13

    Bai, P. et al. Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling. Nat. Commun. 6, 5912 (2015).

    CAS  Article  Google Scholar 

  14. 14

    Farha, O. K. et al. De novo synthesis of a metal–organic framework material featuring ultrahigh surface area and gas storage capacities. Nat. Chem. 2, 944–948 (2010).

    CAS  Article  Google Scholar 

  15. 15

    Ceder, G. Opportunities and challenges for first-principles materials design and applications to Li battery materials. MRS Bull. 35, 693–701 (2010).

    CAS  Article  Google Scholar 

  16. 16

    Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).

    CAS  Article  Google Scholar 

  17. 17

    Aydinol, M., Kohan, A., Ceder, G., Cho, K. & Joannopoulos, J. Ab initio study of lithium intercalation in metal oxides and metal dichalcogenides. Phys. Rev. B 56 1354–1365 (1997).

    CAS  Google Scholar 

  18. 18

    Ceder, G. et al. Identification of cathode materials for lithium batteries guided by first-principles calculations. Nature 392, 694–696 (1998). This paper presented the first demonstration that density functional theory could be used to practically tune the voltage of Li-ion battery electrode materials.

    CAS  Article  Google Scholar 

  19. 19

    Van Der Ven, A., Aydinol, M. K. & Ceder, G. First-principles evidence for stage ordering in LixCoO2 . J. Electrochem. Soc. 145, 2149–2155 (1998).

    CAS  Article  Google Scholar 

  20. 20

    Zhou, F., Cococcioni, M., Kang, K. & Ceder, G. The Li intercalation potential of LiMPO4 and LiMSiO4 olivines with M = Fe, Mn, Co, Ni. Electrochem. Commun. 6, 1144–1148 (2004).

    CAS  Article  Google Scholar 

  21. 21

    Van Der Ven, A. & Ceder, G. Lithium diffusion in layered LixCoO2 . Electrochem. Solid-State Lett. 3, 301–304 (2000).

    CAS  Article  Google Scholar 

  22. 22

    Ong, S., Wang, L., Kang, B. & Ceder, G. Li–Fe–P–O2 phase diagram from first principles calculations. Chem. Mater. 20, 1798–1807 (2008).

    CAS  Article  Google Scholar 

  23. 23

    Ong, S. P., Jain, A., Hautier, G., Kang, B. & Ceder, G. Thermal stabilities of delithiated olivine MPO4 (M = Fe, Mn) cathodes investigated using first principles calculations. Electrochem. Commun. 12, 427–430 (2010).

    CAS  Article  Google Scholar 

  24. 24

    Kang, K., Meng, Y. S., Bréger, J., Grey, C. P. & Ceder, G. Electrodes with high power and high capacity for rechargeable lithium batteries. Science 311, 977–980 (2006).

    CAS  Article  Google Scholar 

  25. 25

    Ong, S. P. et al. Phase stability, electrochemical stability and ionic conductivity of the Li10±1MP2X12 (M = Ge, Si, Sn, Al or P, and X = O, S or Se) family of superionic conductors. Energy Environ. Sci. 12, 148–156 (2012).

    Google Scholar 

  26. 26

    Jain, A. et al. A high-throughput infrastructure for density functional theory calculations. Comput. Mater. Sci. 50, 2295–2310 (2011).

    CAS  Article  Google Scholar 

  27. 27

    Kim, J. C. et al. Synthesis and electrochemical properties of monoclinic LiMnBO3 as a Li intercalation material. J. Electrochem. Soc. 158, A309–A315 (2011).

    CAS  Article  Google Scholar 

  28. 28

    Jain, A. et al. A computational investigation of Li9M3(P2O7)3(PO4)2 (M = V, Mo) as cathodes for Li ion batteries. J. Electrochem. Soc. 159, A622–A633 (2012).

    CAS  Article  Google Scholar 

  29. 29

    Ma, X., Hautier, G., Jain, A., Doe, R. & Ceder, G. Improved capacity retention for LiVO2 by Cr substitution. J. Electrochem. Soc. 160, A279–A284 (2012).

    Article  CAS  Google Scholar 

  30. 30

    Hautier, G. et al. Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations. J. Mater. Chem. 21, 17147–17153 (2011).

    CAS  Article  Google Scholar 

  31. 31

    Hautier, G., Fischer, C., Ehrlacher, V., Jain, A. & Ceder, G. Data mined ionic substitutions for the discovery of new compounds. Inorg. Chem. 50, 656–663 (2011).

    CAS  Article  Google Scholar 

  32. 32

    Bergerhoff, G., Hundt, R., Sievers, R. & Brown, I. The inorganic crystal structure data base. J. Chem. Inf. Comput. Sci. 23, 66–69 (1983).

    CAS  Article  Google Scholar 

  33. 33

    Chen, H. et al. Carbonophosphates: a new family of cathode materials for Li-ion batteries identified computationally. Chem. Mater. 24, 2009–2016 (2012).

    CAS  Article  Google Scholar 

  34. 34

    Chen, H., Hautier, G. & Ceder, G. Synthesis, computed stability, and crystal structure of a new family of inorganic compounds: carbonophosphates. J. Am. Chem. Soc. 134, 19619–19627 (2012).

    CAS  Article  Google Scholar 

  35. 35

    Chen, H. et al. Sidorenkite (Na3MnPO4CO3): a new intercalation cathode material for Na-ion batteries. Chem. Mater. 25, 2777–2786 (2013).

    CAS  Article  Google Scholar 

  36. 36

    Huang, W. et al. Detailed investigation of Na2.24FePO4CO3 as a cathode material for Na-ion batteries. Sci. Rep. 4, 4188 (2014).

    Article  CAS  Google Scholar 

  37. 37

    Anasori, B. et al. Two-dimensional, ordered, double transition metals carbides (MXenes). ACS Nano 9, 9507–9516 (2015).

    CAS  Article  Google Scholar 

  38. 38

    Schlapbach, L. & Züttel, A. Hydrogen-storage materials for mobile applications. Nature 414, 353–358 (2001).

    CAS  Article  Google Scholar 

  39. 39

    Besenbacher, F. et al. Design of a surface alloy catalyst for steam reforming. Science 279, 1913–1915 (1998).

    CAS  Article  Google Scholar 

  40. 40

    Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I. B. & Nørskov, J. K. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nat. Mater. 5, 909–913 (2006). This report provided an early example of using density functional theory for ‘virtual screening’ here applied to catalytic materials.

    CAS  Article  Google Scholar 

  41. 41

    Medford, A. J. et al. From the Sabatier principle to a predictive theory of transition-metal heterogeneous catalysis. J. Catal. 328, 36–42 (2015).

    CAS  Article  Google Scholar 

  42. 42

    Yan, J. et al. Materials descriptors for predicting thermoelectric performance. Energy Environ. Sci. 8, 983–994 (2015).

    Google Scholar 

  43. 43

    Persson, K. A., Waldwick, B., Lazic, P. & Ceder, G. Prediction of solid-aqueous equilibria: scheme to combine first-principles calculations of solids with experimental aqueous states. Phys. Rev. B 85, 235438 (2012).

    Article  CAS  Google Scholar 

  44. 44

    Sun, W., Wolverton, C. & Akbarzadeh, A. First-principles prediction of high-capacity, thermodynamically reversible hydrogen storage reactions based on (NH4)2B12H12 . Phys. Rev. B 83, 064112 (2011).

    Article  CAS  Google Scholar 

  45. 45

    Siegel, D., Wolverton, C. & Ozolins, V. Thermodynamic guidelines for the prediction of hydrogen storage reactions and their application to destabilized hydride mixtures. Phys. Rev. B 76, 134102 (2007).

    Article  CAS  Google Scholar 

  46. 46

    Wolverton, C., Siegel, D. J., Akbarzadeh, A. R. & Ozolins, V. Discovery of novel hydrogen storage materials: an atomic scale computational approach. J. Phys. Condens. Matter 20, 064228 (2008).

    CAS  Article  Google Scholar 

  47. 47

    Alapati, S. V., Johnson, J. K. & Sholl, D. S. Identification of destabilized metal hydrides for hydrogen storage using first principles calculations. J. Phys. Chem. B 110, 8769–8776 (2006).

    CAS  Article  Google Scholar 

  48. 48

    Lu, J., Fang, Z., Choi, Y. & Sohn, H. Potential of binary lithium magnesium nitride for hydrogen storage applications. J. Phys. Chem. C 111, 12129–12134 (2007).

    CAS  Article  Google Scholar 

  49. 49

    Lu, J., Choi, Y. J., Fang, Z. Z. & Sohn, H. Y. Effect of milling intensity on the formation of LiMgN from the dehydrogenation of LiNH2–MgH2 (1:1) mixture. J. Power Sources 195, 1992–1997 (2010).

    CAS  Article  Google Scholar 

  50. 50

    Osborn, W., Markmaitree, T. & Shaw, L. L. Evaluation of the hydrogen storage behavior of a LiNH2+MgH2 system with 1:1 ratio. J. Power Sources 172, 376–378 (2007).

    CAS  Article  Google Scholar 

  51. 51

    Liu, Y. et al. Hydrogen storage in a LiNH2–MgH2 (1:1) system. Chem. Mater. 20, 3521–3527 (2008).

    CAS  Article  Google Scholar 

  52. 52

    Mazin, I. I. Superconductivity gets an iron boost. Nature 464, 183–186 (2010).

    CAS  Article  Google Scholar 

  53. 53

    Kortus, J., Mazin, I. I., Belashchenko, K. D., Antropov, V. P. & Boyer, L. L. Superconductivity of metallic boron in MgB2 . Phys. Rev. Lett. 86, 4656–4659 (2001).

    CAS  Article  Google Scholar 

  54. 54

    Floris, A. et al. Superconducting properties of MgB2 from first principles. Phys. Rev. Lett. 94, 037004 (2005).

    CAS  Article  Google Scholar 

  55. 55

    Mazin, I. I., Singh, D. J., Johannes, M. D. & Du, M. H. Unconventional superconductivity with a sign reversal in the order parameter of LaFeAsO1−xFx . Phys. Rev. Lett. 101, 057003 (2008).

    CAS  Article  Google Scholar 

  56. 56

    Chang, K. J. & Cohen, M. M. L. Structural and electronic properties of the high-pressure hexagonal phases of Si. Phys. Rev. B 30, 5376–5378 (1984).

    CAS  Article  Google Scholar 

  57. 57

    Chang, K. J. et al. Superconductivity in high-pressure metallic phases of Si. Phys. Rev. Lett. 54, 2375–2378 (1985).

    CAS  Article  Google Scholar 

  58. 58

    Liu, A. Y. & Cohen, M. L. Electron-phonon coupling in bcc and 9R lithium. Phys. Rev. B 44, 9678–9684 (1991).

    CAS  Article  Google Scholar 

  59. 59

    Neaton, J. B. J. & Ashcroft, N. W. Pairing in dense lithium. Nature 400, 141–144 (1999).

    CAS  Article  Google Scholar 

  60. 60

    Christensen, N. E. & Novikov, D. L. Predicted superconductive properties of lithium under pressure. Phys. Rev. Lett. 86, 1861–1864 (2001).

    CAS  Article  Google Scholar 

  61. 61

    Shimizu, K., Ishikawa, H., Takao, D., Yagi, T. & Amaya, K. Superconductivity in compressed lithium at 20 K. Nature 419, 597–599 (2002).

    CAS  Article  Google Scholar 

  62. 62

    Struzhkin, V. V., Eremets, M. I., Gan, W., Mao, H.-k. & Hemley, R. J. Superconductivity in dense lithium. Science 298, 1213–1215 (2002).

    CAS  Article  Google Scholar 

  63. 63

    Deemyad, S. & Schilling, J. S. Superconducting phase diagram of Li metal in nearly hydrostatic pressures up to 67 GPa. Phys. Rev. Lett. 91, 167001 (2003).

    Article  CAS  Google Scholar 

  64. 64

    Kolmogorov, A. N. et al. New superconducting and semiconducting Fe-B compounds predicted with an ab initio evolutionary search. Phys. Rev. Lett. 105, 217003 (2010).

    CAS  Article  Google Scholar 

  65. 65

    Gou, H. et al. Discovery of a superhard iron tetraboride superconductor. Phys. Rev. Lett. 111, 157002 (2013).

    Article  CAS  Google Scholar 

  66. 66

    Li, Y., Hao, J., Liu, H., Li, Y. & Ma, Y. The metallization and superconductivity of dense hydrogen sulfide. J. Chem. Phys. 140, 174712 (2014).

    Article  CAS  Google Scholar 

  67. 67

    Drozdov, A. P., Eremets, M. I., Troyan, I. A., Ksenofontov, V. & Shylin, S. I. Conventional superconductivity at 203 kelvin at high pressures in the sulfur hydride system. Nature 525, 73–76 (2015).

    CAS  Article  Google Scholar 

  68. 68

    Ashcroft, N. Hydrogen dominant metallic alloys: high temperature superconductors? Phys. Rev. Lett. 92, 187002 (2004).

    CAS  Article  Google Scholar 

  69. 69

    Eremets, M. I., Troyan, I. A., Medvedev, S. A., Tse, J. S. & Yao, Y. Superconductivity in hydrogen dominant materials: silane. Science 319, 1506–1509 (2008).

    CAS  Article  Google Scholar 

  70. 70

    Pickard, C. J. & Needs, R. J. High-pressure phases of silane. Phys. Rev. Lett. 97, 045504 (2006).

    Article  CAS  Google Scholar 

  71. 71

    Hanfland, M., Proctor, J. E., Guillaume, C. L., Degtyareva, O. & Gregoryanz, E. High-pressure synthesis, amorphization, and decomposition of silane. Phys. Rev. Lett. 106, 095503 (2011).

    Article  CAS  Google Scholar 

  72. 72

    Richard, C. et al. Renewable energy data book (US Department of Energy, 2013).

    Google Scholar 

  73. 73

    Green, M. A., Emery, K., Hishikawa, Y., Warta, W. & Dunlop, E. D. Solar cell efficiency tables (version 45). Prog. Photovoltaics 23, 1–9 (2015).

    Article  Google Scholar 

  74. 74

    Yu, L. & Zunger, A. Identification of potential photovoltaic absorbers based on first-principles spectroscopic screening of materials. Phys. Rev. Lett. 108, 068701 (2012).

    Article  CAS  Google Scholar 

  75. 75

    Yu, L., Kokenyesi, R. S., Keszler, D. A. & Zunger, A. Inverse design of high absorption thin-film photovoltaic materials. Adv. Energy Mater. 3, 43–48 (2013).

    CAS  Article  Google Scholar 

  76. 76

    Levi, B. G. Nobel prize in Chemistry salutes the discovery of conducting polymers. Phys. Today 53, 19–22 (2000).

    Google Scholar 

  77. 77

    Davis, W., Svec, W., Ratner, M. & Wasielewski, M. Molecular-wire behaviour in p-phenylenevinylene oligomers. Nature 396, 60–63 (1998).

    CAS  Article  Google Scholar 

  78. 78

    Roncali, J. Conjugated poly(thiophenes) — synthesis, functionalization, and applications. Chem. Rev. 92, 711–738 (1992).

    CAS  Article  Google Scholar 

  79. 79

    Peters, C. H. et al. High efficiency polymer solar cells with long operating lifetimes. Adv. Energy Mater. 1, 491–494 (2011).

    CAS  Article  Google Scholar 

  80. 80

    Mühlbacher, D. et al. High photovoltaic performance of a low-bandgap polymer. Adv. Mater. 18, 2884–2889 (2006).

    Article  Google Scholar 

  81. 81

    Heeger, A. J. Semiconducting polymers: the third generation. Chem. Soc. Rev. 39, 2354–2371 (2010).

    CAS  Article  Google Scholar 

  82. 82

    Cohen, A. J., Mori-Sánchez, P. & Yang, W. Insights into current limitations of density functional theory. Science 321, 792–794 (2008).

    CAS  Article  Google Scholar 

  83. 83

    Bedard-Hearn, M. J., Sterpone, F. & Rossky, P. J. Nonadiabatic simulations of exciton dissociation in poly-p-phenylenevinylene oligomers. J. Phys. Chem. A 114, 7661–7670 (2010).

    CAS  Article  Google Scholar 

  84. 84

    Hannewald, K. et al. Theory of polaron bandwidth narrowing in organic molecular crystals. Phys. Rev. B 69, 075211 (2004).

    Article  CAS  Google Scholar 

  85. 85

    Shin, Y. & Lin, X. Modeling photoinduced charge transfer across π-conjugated heterojunctions. J. Phys. Chem. C 117, 12432–12437 (2013).

    CAS  Article  Google Scholar 

  86. 86

    Hachmann, J. et al. The Harvard Clean Energy Project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2, 2241–2251 (2011).

    CAS  Article  Google Scholar 

  87. 87

    Körzdörfer, T. & Brédas, J.-L. Organic electronic materials: recent advances in the DFT description of the ground and excited states using tuned range-separated hybrid functionals. Acc. Chem. Res. 47, 3284–3291 (2014).

    Article  CAS  Google Scholar 

  88. 88

    Sokolov, A. N. et al. From computational discovery to experimental characterization of a high hole mobility organic crystal. Nat. Commun. 2, 437 (2011).

    Article  CAS  Google Scholar 

  89. 89

    Blouin, N. et al. Toward a rational design of poly(2,7carbazole) derivatives for solar cells. J. Am. Chem. Soc. 130, 732–742 (2008).

    CAS  Article  Google Scholar 

  90. 90

    Shin, Y., Liu, J., Quigley, J. J., Luo, H. & Lin, X. Combinatorial design of copolymer donor materials for bulk heterojunction solar cells. ACS Nano 8, 6089–6096 (2014).

    CAS  Article  Google Scholar 

  91. 91

    Hautier, G., Miglio, A., Ceder, G., Rignanese, G.-M. & Gonze, X. Identification and design principles of low hole effective mass p-type transparent conducting oxides. Nat. Commun. 4, 2292 (2013).

    Article  CAS  Google Scholar 

  92. 92

    Bathia, A. et al. High-mobility bismuth-based transparent p-type oxide from high-throughput material screening. Preprint at http://arXiv.org/abs/1412.4429 (2014).

  93. 93

    Yan, F. et al. Design and discovery of a novel half-Heusler transparent hole conductor made of all-metallic heavy elements. Nat. Commun. 6, 7308 (2015).

    CAS  Article  Google Scholar 

  94. 94

    Tritt, T. & Subramanian, M. Thermoelectric materials, phenomena, and applications: a bird's eye view. MRS Bull. 31, 188–198 (2006).

    Article  Google Scholar 

  95. 95

    Pei, Y. et al. Convergence of electronic bands for high performance bulk thermoelectrics. Nature 473, 66–69 (2011).

    CAS  Article  Google Scholar 

  96. 96

    Qiu, B. et al. First-principles simulation of electron mean-free-path spectra and thermoelectric properties in silicon. Europhys. Lett. 109, 57006 (2015).

    Article  CAS  Google Scholar 

  97. 97

    Madsen, G. K. H. Automated search for new thermoelectric materials: the case of LiZnSb. J. Am. Chem. Soc. 128, 12140–12146 (2006). This paper established the general methodology for computational screening of thermoelectric materials, which has inspired several extensions and further studies.

    CAS  Article  Google Scholar 

  98. 98

    Toberer, E. S., May, A. F., Scanlon, C. J. & Snyder, G. J. Thermoelectric properties of p-type LiZnSb: assessment of ab initio calculations. J. Appl. Phys. 105, 063701 (2009).

    Article  CAS  Google Scholar 

  99. 99

    Gorai, P., Parilla, P., Toberer, E. S. & Stevanovic, V. Computational exploration of the binary A1B2 chemical space for thermoelectric performance. Chem. Mater. 27, 6213–6221 (2015).

    CAS  Article  Google Scholar 

  100. 100

    Wang, S., Wang, Z., Setyawan, W., Mingo, N. & Curtarolo, S. Assessing the thermoelectric properties of sintered compounds via high-throughput ab initio calculations. Phys. Rev. X 1, 021012 (2011).

    Google Scholar 

  101. 101

    Zhu, H. et al. Computational and experimental investigation of TmAgTe2 and XYZ2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening. J. Mater. Chem. C 3, 10554–10565 (2015).

    CAS  Google Scholar 

  102. 102

    Sharma, V. et al. Rational design of all organic polymer dielectrics. Nat. Commun. 5, 4845 (2014).

    CAS  Article  Google Scholar 

  103. 103

    Pilania, G. et al. New group IV chemical motifs for improved dielectric permittivity of polyethylene. J. Chem. Inf. Model. 53, 879–886 (2013).

    CAS  Article  Google Scholar 

  104. 104

    Wang, C. C., Pilania, G. & Ramprasad, R. Dielectric properties of carbon-, silicon-, and germanium-based polymers: a first-principles study. Phys. Rev. B 87, 035103 (2013).

    Article  CAS  Google Scholar 

  105. 105

    Ma, R. et al. Rational design and synthesis of polythioureas as capacitor dielectrics. J. Mater. Chem. A 3, 14845–14852 (2015).

    CAS  Article  Google Scholar 

  106. 106

    Bentien, A., Madsen, G., Johnsen, S. & Iversen, B. Experimental and theoretical investigations of strongly correlated FeSb2−xSnx . Phys. Rev. B 74, 205105 (2006).

    Article  CAS  Google Scholar 

  107. 107

    Bentien, A., Johnsen, S., Madsen, G. K. H., Iversen, B. B. & Steglich, F. Colossal Seebeck coefficient in strongly correlated semiconductor FeSb2 . Europhys. Lett. 80, 17008 (2007).

    Article  CAS  Google Scholar 

  108. 108

    Kojima, A., Teshima, K., Shirai, Y. & Miyasaka, T. Organometal halide perovskites as visible-light sensitizers for photovoltaic cells. J. Am. Chem. Soc. 131, 6050–6051 (2009).

    CAS  Article  Google Scholar 

  109. 109

    Yin, W.-J., Yang, J.-H., Kang, J., Yan, Y. & Wei, S.-H. Halide perovskite materials for solar cells: a theoretical review. J. Mater. Chem. A 3, 8926–8942 (2015).

    CAS  Article  Google Scholar 

  110. 110

    Perdew, J. P., Ruzsinszky, A., Constantin, L. A., Sun, J. & Csonka, G. I. Some fundamental issues in ground-state density functional theory: a guide for the perplexed. J. Chem. Theory Comput. 5, 902–908 (2009).

    CAS  Article  Google Scholar 

  111. 111

    Burke, K. Perspective on density functional theory. J. Chem. Phys. 136, 150901 (2012).

    Article  CAS  Google Scholar 

  112. 112

    Cohen, A. J., Mori-Sanchez, P. & Yang, W. Challenges for density functional theory. Chem. Rev. 112, 289–320 (2012).

    CAS  Article  Google Scholar 

  113. 113

    Fonseca Guerra, C., Snijders, J. G., Te Velde, G. & Baerends, E. J. Towards an order-N DFT method. Theor. Chem. Acc. 99, 391–403 (1998).

    Google Scholar 

  114. 114

    Baroni, S., Gironcoli, S. D., Corso, A. D. & Giannozzi, P. Phonons and related crystal properties from density functional perturbation theory. Rev. Mod. Phys. 73, 515 (2001).

    CAS  Article  Google Scholar 

  115. 115

    Sanchez, J. M., Ducastelle, F. & Gratias, D. Generalized cluster description of multicomponent systems. Phys. A 128, 334–350 (1984).

    Article  Google Scholar 

  116. 116

    Runge, E. & Gross, E. K. U. Density functional theory for time-dependent systems. Phys. Rev. Lett. 52, 997–1000 (1984).

    CAS  Article  Google Scholar 

  117. 117

    Petersilka, M., Gossmann, U. & Gross, E. Excitation energies from time-dependent density functional theory. Phys. Rev. Lett. 76, 1212–1215 (1996).

    CAS  Article  Google Scholar 

  118. 118

    Hedin, L. New method for calculating the one-particle Green's function with application to the electron-gas problem. Phys. Rev. 139, A796–A823 (1965).

    Article  Google Scholar 

  119. 119

    Salpeter, E. & Bethe, H. A relativistic equation for bound-state problems. Phys. Rev. 84, 1232–1242 (1951).

    Article  Google Scholar 

  120. 120

    Klimeš J. & Michaelides A. Perspective: advances and challenges in treating van der Waals dispersion forces in density functional theory. J. Chem. Phys. 137 120901 (2012).

    Article  CAS  Google Scholar 

  121. 121

    Carter, E. A. Challenges in modeling materials properties without experimental input. Science 321, 800–803 (2008).

    CAS  Article  Google Scholar 

  122. 122

    Jones, G., Bligaard, T., Abild-Pedersen, F. & Nørskov, J. K. Using scaling relations to understand trends in the catalytic activity of transition metals. J. Phys. Condens. Matter 20, 064239 (2008).

    CAS  Article  Google Scholar 

  123. 123

    Nørskov, J. K., Abild-Pedersen, F., Studt, F. & Bligaard, T. Density functional theory in surface chemistry and catalysis. Proc. Natl Acad. Sci. USA 108, 937–943 (2011).

    Article  Google Scholar 

  124. 124

    Jain, A. et al. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013). Introduction of the Materials Project, today's most popular searchable database of density functional theory calculations used by both experimentalists and theorists.

    Article  CAS  Google Scholar 

  125. 125

    Curtarolo, S. et al. AFLOWLIB.ORG: a distributed materials properties repository from high-throughput ab initio calculations. Comput. Mater. Sci. 58, 227–235 (2012).

    CAS  Article  Google Scholar 

  126. 126

    Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD). JOM 65, 1501–1509 (2013).

    CAS  Article  Google Scholar 

  127. 127

    Landis, D. D. et al. The computational materials repository. Comput. Sci. Eng. 14, 51–57 (2012).

    Article  Google Scholar 

  128. 128

    Oganov, A. R. & Valle, M. How to quantify energy landscapes of solids. J. Chem. Phys. 130, 104504 (2009).

    Article  CAS  Google Scholar 

  129. 129

    Maddox, J. Crystals from first principles. Nature 335, 201 (1988).

    Article  Google Scholar 

  130. 130

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

    Article  CAS  Google Scholar 

  131. 131

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

    Article  CAS  Google Scholar 

  132. 132

    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).

    Article  CAS  Google Scholar 

  133. 133

    Fischer, C. C., Tibbetts, K. J., Morgan, D. & Ceder, G. Predicting crystal structure by merging data mining with quantum mechanics. Nat. Mater. 5, 641–646 (2006).

    CAS  Article  Google Scholar 

  134. 134

    Hautier, G., Fischer, C. C., Jain, A., Mueller, T. & Ceder, G. Finding nature's missing ternary oxide compounds using machine learning and density functional theory. Chem. Mater. 22, 3762–3767 (2010).

    CAS  Article  Google Scholar 

  135. 135

    Meredig, B. et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B 89, 094104 (2014).

    Article  CAS  Google Scholar 

  136. 136

    Meredig, B. & Wolverton, C. A hybrid computational experimental approach for automated crystal structure solution. Nat. Mater. 12, 123–127 (2013).

    CAS  Article  Google Scholar 

  137. 137

    Pickard, C. J. & Needs, R. J. Highly compressed ammonia forms an ionic crystal. Nat. Mater. 7, 775–779 (2008).

    CAS  Article  Google Scholar 

  138. 138

    Ninet, S. et al. Experimental and theoretical evidence for an ionic crystal of ammonia at high pressure. Phys. Rev. B 89, 174103 (2014).

    Article  CAS  Google Scholar 

  139. 139

    Palasyuk, T. et al. Ammonia as a case study for the spontaneous ionization of a simple hydrogen-bonded compound. Nat. Commun. 5, 3460 (2014).

    Article  CAS  Google Scholar 

  140. 140

    Ma, Y. et al. Transparent dense sodium. Nature 458, 182–185 (2009).

    CAS  Article  Google Scholar 

  141. 141

    Fix, T., Sahonta, S.-L., Garcia, V., MacManus-Driscoll, J. L. & Blamire, M. G. Structural and dielectric properties of SnTiO3, a putative ferroelectric. Cryst. Growth Des. 11, 1422–1426 (2011).

    CAS  Article  Google Scholar 

  142. 142

    Gautier, R. et al. Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds. Nat. Chem. 7, 308–316 (2015).

    CAS  Article  Google Scholar 

  143. 143

    Bron, P. et al. Li10SnP2S12: an affordable lithium superionic conductor. J. Am. Chem. Soc. 135, 15694–15697 (2013).

    CAS  Article  Google Scholar 

  144. 144

    Studt, F. et al. Discovery of a Ni-Ga catalyst for carbon dioxide reduction to methanol. Nat. Chem. 6, 320–324 (2014).

    CAS  Article  Google Scholar 

  145. 145

    Cole, J. M. et al. Data mining with molecular design rules identifies new class of dyes for dye-sensitised solar cells. Phys. Chem. Chem. Phys. 16, 26684–26690 (2014).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This work was intellectually led by the Materials Project supported by the US Department of Energy Office of Science, Office of Basic Energy Sciences Department under Contract No. DE-AC02-05CH11231. Y.S. thanks the Battery Materials Research Program under the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Vehicle Technologies of the US Department of Energy.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Kristin A. Persson.

Ethics declarations

Competing interests

The authors declare no competing interests.

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jain, A., Shin, Y. & Persson, K. Computational predictions of energy materials using density functional theory. Nat Rev Mater 1, 15004 (2016). https://doi.org/10.1038/natrevmats.2015.4

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing