Inverse design in search of materials with target functionalities

Abstract

Solid-state chemists have been consistently successful in envisioning and making new compounds, often enlisting the tools of theoretical solid-state physics to explain some of the observed properties of the new materials. Here, a new style of collaboration between theory and experiment is discussed, whereby the desired functionality of the new material is declared first and theoretical calculations are then used to predict which stable and synthesizable compounds exhibit the required functionality. Subsequent iterative feedback cycles of prediction–synthesis–characterization result in improved predictions and promise not only to accelerate the discovery of new materials but also to enable the targeted design of materials with desired functionalities via such inverse design.

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Figure 1: Theoretical prediction, experimental synthesis and characterization of previously unreported compounds belonging to the 18e ABX group.
Figure 2: Examples of artificial structures that can be grown.
Figure 3: Direct and inverse approaches for the design of materials.
Figure 4: The three modalities of inverse design of materials.
Figure 5: New material stability tests.

References

  1. 1

    Descartes, R. Meditations on First Philosophy: with Selections from the Objections and Replies (ed. Cottingham, J. ) (Cambridge Univ. Press, 1996).

    Google Scholar 

  2. 2

    Franceschetti, A. & Zunger, A. The inverse band structure problem: find the atomic configuration with given electronic properties. Nature 402, 60–63 (1999).

    CAS  Article  Google Scholar 

  3. 3

    Kurtz, S. & Levi, D. Conversion efficiencies of best research solar cells worldwide from 1976 through 2017 for various photovoltaic technologies. NRELhttps://www.nrel.gov/pv/assets/images/efficiency-chart.png (2017).

  4. 4

    Eperon, G. et al. Perovskite-perovskite tandem photovoltaics with optimized band gaps. Science 20, aaf9717 (2016).

    Google Scholar 

  5. 5

    Buzea, C. & Yamashita, T. Review of the superconducting properties of MgB2. Superconductor Sci. Technol. 14, R115–R146 (2001).

    CAS  Article  Google Scholar 

  6. 6

    Luo, H. et al. Optical and structural properties of single phase epitaxial p-type transparent oxide thin films. Adv. Mater. 21, 3604–3607 (2007).

    Article  CAS  Google Scholar 

  7. 7

    Center for Material Genomics, Material Science, Duke University et al. AFLOW database of calculated materials properties. AFLOWhttp://aflowlib.org/ (2016).

  8. 8

    Materials Project. The Materials Project Database for calculated properties. Materials Projecthttps://www.materialsproject.org/ (2012).

  9. 9

    Wolverton, C. et al. The Open Quantum Materials database for calculated properties. OQMDhttp://oqmd.org/ (2017).

  10. 10

    Villars, P. et al. The Pauling File database of measured materials properties. Pauling Filehttp://paulingfile.com/index.php?p¼home (2016).

  11. 11

    FIZ Karlsruhe. Inorganic Crystal Structure Database (ICSD). FIZ Karlsruhe ICSDhttp://www2.fiz-karlsruhe.de/icsd_home.html (2017).

  12. 12

    Yang, D. et al. Functionality-directed First-principles screening of hybrid organic-inorganic perovskites with target intrinsic photovoltaic functionalities. Chem. Mater. 29, 524 (2017).

    CAS  Article  Google Scholar 

  13. 13

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

    CAS  Article  Google Scholar 

  14. 14

    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 

  15. 15

    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 

  16. 16

    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 

  17. 17

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

    Article  Google Scholar 

  18. 18

    Jain, A., Shin, Y. & Persson, K. A. Computational predictions of energy materials using density functional theory. Nat. Rev. Mater. 1, 15004 (2016).

    CAS  Article  Google Scholar 

  19. 19

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

    CAS  PubMed  Article  Google Scholar 

  20. 20

    Zhang, X., Yu, L., Zakutayev, A. & Zunger, A. Sorting stable versus unstable hypothetical compounds: the case of multi functional ABX Half Heusler filled tetrahedral structure. Adv. Funct. Mater. 22, 1425–1435 (2012).

    CAS  Article  Google Scholar 

  21. 21

    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  PubMed  Article  Google Scholar 

  22. 22

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

    PubMed  Article  CAS  Google Scholar 

  23. 23

    Eigler, D. M. & Schweizer, E. K. Positioning single atoms with a scanning tunnelling microscope. Nature 344, 524–526 (1990).

    CAS  Article  Google Scholar 

  24. 24

    Nadj-Perge, S. et al.Observation of Majorana fermions in ferromagnetic atomic chains on a superconductor. Science 346, 602–607 (2014).

    CAS  PubMed  Article  Google Scholar 

  25. 25

    Sitter, H. & Herman, M. A. Molecular Beam Epitaxy Fundamentals and Current Status (Springer-Verlag, Berlin, Heidelberg, 2012).

    Google Scholar 

  26. 26

    Mundy, J. A. et al Atomically engineered ferroic layers yield a roomtemperature magnetoelectric multiferroic. Nature 537, 523 (2016).

    CAS  PubMed  Article  Google Scholar 

  27. 27

    Caroff, P., et al. Controlled polytypic and twin-plane superlattices in III–V nanowires. Nat. Nanotech. 4, 50. (2009).

    CAS  Article  Google Scholar 

  28. 28

    Algra, R. E., et al., Twinning superlattices in indium phosphide nanowires. Nature 456, 369 (2008).

    CAS  PubMed  Article  Google Scholar 

  29. 29

    Ulloa, J. M., Koenraad, P. M., Bonnet-Eymard, M., Létoublon, A. & Bertru, N. Effect of a lattice-matched GaAsSb capping layer on the structural properties of InAs/InGaAs/InP quantum dots. J. Appl. Phys.107, 074309 (2010).

    Article  CAS  Google Scholar 

  30. 30

    Yoshioka, D. The Quantum Hall Effect (Springer-Verlag, Berlin, Heidelberg, 2002).

    Google Scholar 

  31. 31

    Ciorga, M. et al. Coulomb and spin blockade of two few-electron quantum dots in series in the cotunneling regime, Phys. Rev. B 70, 235306 (2004).

    Article  CAS  Google Scholar 

  32. 32

    Hwang, H. Y. et al. Emergent phenomena at oxide interfaces. Nat. Mater. 11, 103–113 (2012).

    CAS  PubMed  Article  Google Scholar 

  33. 33

    Liu, Q., Zhang, X., Abdalla, L. B. & Zunger, A. Transforming common III-V and II-VI semiconductor compounds into two-dimensional topological heterostructures: the case of CdTe/InSb superlattices. Adv. Funct. Mater. 26, 3259–3267 (2016).

    CAS  Article  Google Scholar 

  34. 34

    d'Avezac, M., Luo, J. W., Chanier, T. & Zunger, A. Genetic-algorithm discovery of a direct-gap and optically allowed superstructure from indirect-gap Si and Ge semiconductors. Phys. Rev. Lett. 108, 027401 (2012).

    PubMed  Article  CAS  Google Scholar 

  35. 35

    Zhang, L., d'Avezac, M., Luo, J. W. & Zunger, A. Genomic design of strong direct-gap optical transition in Si/Ge core/multishell nanowires. Nanoletters 12, 984–991 (2012).

    CAS  Article  Google Scholar 

  36. 36

    Oh, Y. J., Lee, I.-H., Kim, S., Lee, J. & Chang, K. J. Dipole-allowed direct band gap silicon superlattices. Sci. Rep. 5, 18086 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37

    Lee, I.-H., Lee, J., Oh, Y. J., Kim, S. & Chang, K. J. Computational search for direct band gap silicon crystal. Phys. Rev. Lett. 90, 115209 (2014).

    Google Scholar 

  38. 38

    Guo, Y., Wang, Q., Kawazoe, Y. & Jena, P. A new silicon phase with direct band gap and novel optoelectronic properties. Sci. Rep. 5, 14342 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39

    Piquini, P., Graf, P. A. & Zunger, A. Band gap design of quarternary (In, Ga)(As, Sb) semiconductors via the inverse band structure approach. Phys. Rev. Lett. 100, 186403 (2008).

    PubMed  Article  CAS  Google Scholar 

  40. 40

    Zhang, L., Luo, J. W., Saraiva, A., Koiller, B. & Zunger, A. Genetic design of enhanced valley splitting towards a spin qubit in silicon. Nat. Commun. 4, 2396 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41

    Deng, J., Zunger, A. & Liu, J. Z. Cation ordering induced polarization enhancement for PbTiO3-SrTiO3 ferroelectric-dielectric superlattices. Phys. Rev. B 91, 081302 (2015)

    Article  CAS  Google Scholar 

  42. 42

    Lewerenz, H.-J. & Peter, L. Photoelectrochemical Water Splitting: Materials, Processes and Architectures (RSC Publishing, 2013).

    Google Scholar 

  43. 43

    Luque, A., Hegedus, S., Delahoy, A. E. & Guo, S. Transparent Conducting Oxides for Photovoltaics (John Wiley, 2011).

    Google Scholar 

  44. 44

    Sarmadian, N. et al. High throughput first principles calculations of bixbyite oxides for TCO applications. Phys. Chem. Chem. Phys. 16, 17724–17733 (2014).

    CAS  PubMed  Article  Google Scholar 

  45. 45

    Zhao, L.-D. et al. Thermoelectrics with earth abundant elements: high performance P-Type PbS nanostructured with SrS and CaS. J. Am. Chem. Soc. 134, 7902–7912 (2012).

    CAS  PubMed  Article  Google Scholar 

  46. 46

    Emery, A. A., Saal, J. E., Kirklin, S., Hegde, V. I. & Wolverton, C. High-throughput computational screening of perovskites for thermochemical water splitting applications. Chem. Mater. 28, 5621–5634 (2016).

    CAS  Article  Google Scholar 

  47. 47

    Zhao, L.-D. et al. Ultrahigh power factor and thermoelectric performance in hole-doped single-crystal SnSe. Science 351, 141–144 (2016).

    CAS  PubMed  Article  Google Scholar 

  48. 48

    Peng, H. et al. Li-doped Cr2MnO4: a new p-type transparent conducting oxide by computational materials design. Adv. Funct. Mater. 23, 5267–5276 (2013).

    CAS  Article  Google Scholar 

  49. 49

    Varley, J. B. et al. High-throughput design of non-oxide p-type transparent conducting materials: data mining, search strategy, and identification of boron phosphide. Chem. Mater. 29, 2568–2573 (2017).

    CAS  Article  Google Scholar 

  50. 50

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  51. 51

    Zhang, X., Zhang, L., Perkins, J. D. & Zunger, A. Intrinsic transparent conductors without doping. Phys. Rev. Lett. 115, 176602 (2015).

    PubMed  Article  CAS  Google Scholar 

  52. 52

    Walsh, A. & Zunger, A. Instilling defect tolerance in new compounds. Nat. Mater. 16, 964 (2017).

    CAS  Article  Google Scholar 

  53. 53

    Dudiy, S. V. & Zunger, A. Searching for alloy configurations with target physical properties: impurity design via a genetic algorithm inverse band structure approach. Phys. Rev. Lett. 97, 046401 (2006).

    CAS  PubMed  Article  Google Scholar 

  54. 54

    Franceschetti, A., Dudiy, S., Barabash, S., Zunger, A. & van Schiffgaarde, M. First-principles combinatorial design of transition temperature in multicomponent systems: the case of Mn in GaAs. Phys. Rev. Lett. 97, 047202 (2006).

    CAS  PubMed  Article  Google Scholar 

  55. 55

    Anderson, P. W. Concepts in Solids (Frontiers in Physics) (Benjamin, 1963).

    Google Scholar 

  56. 56

    Suzuk, S., Inoue, J. & Chakrabarti, B. K. Quantum Ising Phases and Transitions in Transverse Ising Models (Lecture Notes in Physics) 2nd edn (Springer-Verlag, Berlin, Heidelberg, 2013).

    Google Scholar 

  57. 57

    Derzhko, O., Richter, J. & Maksymenko, M. Strongly correlated flat-band systems: the route from Heisenberg spins to Hubbard electrons. Int. J. Modern Phys. B 29, 1530007 (2015).

    Article  Google Scholar 

  58. 58

    Justi, R. & Gilbert, J. History and philosophy of science through models: some challenges in the case of ‘the atom’. Int. J. Sci. Educ. 22, 993–1009 (2000).

    Article  Google Scholar 

  59. 59

    Luttinger, J. M. & Kohn, W. Motion of electrons and holes in perturbed periodic fields. Phys. Rev. 97, 869 (1955).

    CAS  Article  Google Scholar 

  60. 60

    Witczak-Krempa, W., Chen, G., Kim, Y. B. & Balents, L. Correlated quantum phenomena in the strong spin-orbit regime. Annu. Rev. Condens. Matter Phys. 5, 57–82 (2014).

    CAS  Article  Google Scholar 

  61. 61

    Konig, M. et al. Quantum spin hall insulator state in HgTe quantum wells. Science 318, 766–770 (2007).

    PubMed  Article  CAS  Google Scholar 

  62. 62

    Ediger, M. et al. Peculiar many-body effects revealed in the spectroscopy of heavily charged quantum dots. Nat. Phys. 3, 774–779 (2007).

    CAS  Article  Google Scholar 

  63. 63

    Mlinar, V. & Zunger, A. Spectral barcoding of quantum dots: Deciphering structural motifs from the excitonic spectra. Phys. Rev. B 80, 035328 (2009).

    Article  CAS  Google Scholar 

  64. 64

    Mlinar, V. et al. Structure of quantum dots as seen by excitonic spectroscopy versus structural characterization: Using theory to close the loop. Phys. Rev. B 80, 165425 (2009).

    Article  CAS  Google Scholar 

  65. 65

    Akopian, N. et al Entangled photon pairs from semiconductor quantum dots. Phy. Rev. Lett. 96, 13050 (2006).

    Article  CAS  Google Scholar 

  66. 66

    Georges, A., Kotliar, G., Krauth, W. & Rozenberg, M. J. Dynamical mean-field theory of strongly correlated fermion systems and the limit of infinite dimensions. Rev. Modern Phys. 68, 13 (1996).

    CAS  Article  Google Scholar 

  67. 67

    Kent, P. R. C., Needs, R. J. & Rajagopal, G. Monte Carlo energy and variance-minimization techniques for optimizing many-body wave functions. Phys. Rev. B 59, 12344 (1999).

    CAS  Article  Google Scholar 

  68. 68

    Hart, G., Blum, V., Walorski, M. & Zunger, A. Genetic determination of first-principles Hamiltonians. Nat. Mater. 4, 391 (2005).

    CAS  PubMed  Article  Google Scholar 

  69. 69

    Rabe, K. M. & Waghmare, U. V. First-principles model Hamiltonians for ferroelectric transitions. Ferroelectrics 151, 69 (1994).

    Article  Google Scholar 

  70. 70

    Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiment. Nature 533, 73–76 (2016).

    CAS  PubMed  Article  Google Scholar 

  71. 71

    Pilania, G., Balachandran, P. V., Gubernatis, J. E. & Lookman, T. Classification of ABO3 perovskite solids: a machine learning study. Acta Cryst. B 71, 507 (2015).

    CAS  Article  Google Scholar 

  72. 72

    Hansen, K. et al. Assessment and validation of machine learning methods for predicting molecular atomization energies. J. Chem. Theory Comput. 9, 3404–3419 (2013).

    CAS  PubMed  Article  Google Scholar 

  73. 73

    Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, 19375 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74

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

    Article  Google Scholar 

  75. 75

    Zunger, A. in Statics and in Dynamics of Alloy Phase Transformations (eds Turchi, P. E. A. & Gonis, A. ) 361–419 (Springer Science+Business Media, 1994).

    Google Scholar 

  76. 76

    Wang, S. et al. Assessing the thermoelectric properties of sintered compounds via high throughput ab initio calculations. Phys. Rev. X 1, 021012 (2001).

    Google Scholar 

  77. 77

    Mooser, E. & Pearson, W. B. On the crystal chemistry of normal valence compounds. Acta Crystallograph. 12, 1015 (1959).

    CAS  Article  Google Scholar 

  78. 78

    Zunger, A. Systematization of the stable crystal structure of all AB-type binary compounds: A pseudopotential orbital-radii approach. Phys. Rev. B 22, 5832 (1980).

    Google Scholar 

  79. 79

    Pettifor, D. Phenomenological and microscopic theories of structural stability. J. Less Common Metals 114, 7 (1985).

    CAS  Article  Google Scholar 

  80. 80

    Sabatier, P. C. Past and future of inverse problems. J. Math. Phys. 41, 4082 (2000).

    Article  Google Scholar 

  81. 81

    Wood, D. M. & Zunger, A. Epitaxial effects on coherent phase diagrams of alloys. Phys. Rev. B 40, 4062 (1989).

    CAS  Article  Google Scholar 

  82. 82

    Zunger, A. in Handbook of Crystal Growth (ed. Hurle, D. T. J. ) 998–1047 (Elsevier, 1994).

    Google Scholar 

  83. 83

    d'Avezac, M. M. & Zunger, A. Identifying the minimum-energy atomic configuration on a lattice: a Lamarckian twist on Darwinian evolution. Phys. Rev. B 78, 064102 (2008).

    Article  CAS  Google Scholar 

  84. 84

    Glover, F. & Hanafi, S. Metaheuristic search with inequalities and target objectives for mixed binary optimization. Int. J. Appl. Metaheurist. Comput. 1, 1–17 (2010).

    Google Scholar 

  85. 85

    Bendsøe, M. P. & Kikuchi, N. Generating optimal topologies in structural design using a homogenization method. Computer Methods Appl. Mechan. Engineer. 71, 197–224 (1988).

    Article  Google Scholar 

  86. 86

    Wang, M., Hu, X., Beratan, D. N. & Yang, W. Designing molecules by optimizing potentials. J. Am. Chem. Soc. 128, 3228 (2006).

    CAS  PubMed  Article  Google Scholar 

  87. 87

    Wang, M., Hu, X., Beratan, D. N. & Yang, W. Designing molecules with optimal properties using the linear combination of atomic potentials approach in an am1 semi-empirical framework. J. Am. Chem. Soc. 111, 146 (2006).

    Google Scholar 

  88. 88

    De Gironcoli, S., Giannozzi, P. & Baroni, S. Phonon dispersions in GaxAl1–x As alloys. Phys. Rev. Lett. 66, 2116 (1990).

    Article  Google Scholar 

  89. 89

    Liu, Q. & Zunger, A. Predicted realization of cubic dirac fermion in quasi-one-dimensional transition-metal mono chalcogenides. Phys. Rev. X 7, 021019 (2017).

    Google Scholar 

  90. 90

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  91. 91

    Ashton, M., Paul, J., Sinnott, S. B. & Hennig, R. G. Topology-scaling identification of layered solids and stable exfoliated 2D materials. Phys. Rev. Lett. 118, 106101 (2017).

    PubMed  Article  Google Scholar 

  92. 92

    Cheon, G. et al. Data mining for new two- and one-dimensional weakly bonded solids and lattice-commensurate heterostructures. Nano Lett. 17, 1915–1923 (2017).

    CAS  PubMed  Article  Google Scholar 

  93. 93

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

    CAS  PubMed  Article  Google Scholar 

  94. 94

    Akbarzadeh, A. R., Ozolinš, V. & Wolverton, C. First-principles determination of multicomponent hydride phase diagrams: application to the Li-Mg-N-H system. Adv. Mater. 19, 3233 (2007).

    CAS  Article  Google Scholar 

  95. 95

    Ozolinš, V., Majzoub, H. & Wolverton, C. First-principles prediction of thermodynamically reversible hydrogen storage reactions in the Li-Mg-Ca-B-H system. J. Am. Chem. Soc. 131, 230 (2009).

    PubMed  Article  CAS  Google Scholar 

  96. 96

    Ozolinš, V., Wolverton, C. & Zunger, A. Cu-Au, Ag-Au, Cu-Ag, and Ni-Au intermetallics: first-principles study of temperature-composition phase diagrams and structures. Phys. Rev. B 57, 6427 (1998).

    Article  Google Scholar 

  97. 97

    Barabash, S. V., Ozolinš, V. & Wolverton, C. First-principles theory of competing order types, phase separation, and phonon spectra in thermoelectric AgPbmSbTem+2 alloys. Phys. Rev. B 101, 155704 (2008).

    CAS  Google Scholar 

  98. 98

    Dyer, M. S. et al. Computationally assisted identification of functional inorganic materials. Science 340, 847–852 (2013).

    CAS  PubMed  Article  Google Scholar 

  99. 99

    Perkins, J. D. et al. Inverse design approach to hole doping in ternary oxides: Enhancing p-type conductivity in cobalt oxide spinels. Phys. Rev. B 84, 205207 (2011).

    Article  CAS  Google Scholar 

  100. 100

    Young, S. M. et al. Dirac semimetal in three dimensions Phys. Rev. Lett. 108, 140405 (2012).

    CAS  PubMed  Article  Google Scholar 

  101. 101

    Vidal, J., Zhang, X., Yu, L., Luo, J.-W. & Zunger, A. False-positive and false negative assignments of topological insulators in density functional theory and hybrids. Phys. Rev. B 84, 041109 (2011).

    Article  CAS  Google Scholar 

  102. 102

    Zhang, X., Abdalla, L. B., Liu, Q. & Zunger, A. The enabling electronic motif for topological insulation in ABO3 perovskites. Adv. Funct. Mater. 27, 37 (2017).

    CAS  Google Scholar 

  103. 103

    Ihm, J. J., A. Zunger, A. & Cohen, M. L. A. Momentum space formalism for the total energy of solids using pseudopotentials. J. Phys. C 12, 4409–4421 (1979).

    CAS  Article  Google Scholar 

  104. 104

    Sun, W. et al. The thermodynamic scale of inorganic crystalline metastability. Sci. Adv. 2, 11 (2016).

    Google Scholar 

  105. 105

    Trimarchi, G., Zhang, X., Freeman, A. J. & Zunger, A. Structurally unstable AIIIBiO3 perovskites are predicted to be topological insulators but their stable structural forms are trivial band insulators. Phys. Rev. B 90, 161111 (2014).

    Article  CAS  Google Scholar 

  106. 106

    Trimarchi, G. & Zunger, A. Global space group optimization problem: Finding the stablest crystal structure without constraints. Phys. Rev. B 75, 104113 (2007).

    Article  CAS  Google Scholar 

  107. 107

    Revard, B. C., Tipton, W. W. & Hennig, R. G. Structure and stability prediction of compounds with evolutionary algorithms. Top. Curr. Chem. 345, 181 (2014).

    CAS  PubMed  Article  Google Scholar 

  108. 108

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

    PubMed  Article  CAS  Google Scholar 

  109. 109

    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  PubMed  Article  Google Scholar 

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Acknowledgements

This work was supported by the US Department of Energy, Office of Science, Basic Energy Science, Materials Sciences and Engineering Division under grant no. DE-FG02-13ER46959, and by Energy Efficiency and Renewable Energy (EERE) Sun Shot initiative under DE-EE0007366 and by the National Science foundation NSF Grant No. DMREF-13-34170. This work used resources of the National Energy Research Scientific Computing (NERSC) Center, which is supported by the Office of Science of the U.S. Department of Energy, supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering.

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Zunger, A. Inverse design in search of materials with target functionalities. Nat Rev Chem 2, 0121 (2018). https://doi.org/10.1038/s41570-018-0121

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