1932

Abstract

The performance of thermoelectric materials is determined by their electrical and thermal transport properties that are very sensitive to small modifications of composition and microstructure. Discovery and design of next-generation materials are starting to be accelerated by computational guidance. We review progress and challenges in the development of accurate and efficient first-principles methods for computing transport coefficients and illustrate approaches for both rapid materials screening and focused optimization. Particularly important and challenging are computations of electron and phonon scattering rates that enter the Boltzmann transport equations, and this is where there are many opportunities for improving computational methods. We highlight the first successful examples of computation-driven discoveries of high-performance materials and discuss avenues for tightening the interaction between theoretical and experimental materials discovery and optimization.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-matsci-100520-015716
2021-07-26
2024-05-19
Loading full text...

Full text loading...

/deliver/fulltext/matsci/51/1/annurev-matsci-100520-015716.html?itemId=/content/journals/10.1146/annurev-matsci-100520-015716&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Schwarz K, Blaha P, Madsen GKH. 2002. Electronic structure calculations of solids using the WIEN2k package for material sciences. Comput. Phys. Commun. 147:1–270–76
    [Google Scholar]
  2. 2. 
    Singh DJ, Mazin II. 1997. Calculated thermoelectric properties of La-filled skutterudites. Phys. Rev. B 56:4R1650–53
    [Google Scholar]
  3. 3. 
    Kresse G, Hafner J. 1993. Ab initio molecular dynamics for liquid metals. Phys. Rev. B 47:155861
    [Google Scholar]
  4. 4. 
    Giannozzi P, Baroni S, Bonini N, Calandra M, Car R et al. 2009. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys. Condens. Matter 21:39395502
    [Google Scholar]
  5. 5. 
    Martin RM. 2004. Electronic Structure: Basic Theory and Practical Methods Cambridge, UK: Cambridge Univ. Press
  6. 6. 
    Hybertsen MS, Louie SG. 1986. Electron correlation in semiconductors and insulators: band gaps and quasiparticle energies. Phys. Rev. B. 34:85390–413
    [Google Scholar]
  7. 7. 
    Samsonidze G, Park C-H, Kozinsky B. 2014. Insights and challenges of applying the GW method to transition metal oxides. J. Phys. Condens. Matter 26:47475501
    [Google Scholar]
  8. 8. 
    Nguyen NL, Colonna N, Ferretti A, Marzari N. 2018. Koopmans-compliant spectral functionals for extended systems. Phys. Rev. X 8:2021051
    [Google Scholar]
  9. 9. 
    Chen X, Parker D, Singh DJ. 2013. Importance of non-parabolic band effects in the thermoelectric properties of semiconductors. Sci. Rep. 3:13168
    [Google Scholar]
  10. 10. 
    Mecholsky NA, Resca L, Pegg IL, Fornari M. 2014. Theory of band warping and its effects on thermoelectronic transport properties. Phys. Rev. B 89:15155131
    [Google Scholar]
  11. 11. 
    Pei Y, Shi X, LaLonde A, Wang H, Chen L, Snyder GJ 2011. Convergence of electronic bands for high performance bulk thermoelectrics. Nature 473:734566–69
    [Google Scholar]
  12. 12. 
    Shi X, Yang J, Wu L, Salvador JR, Zhang C et al. 2015. Band structure engineering and thermoelectric properties of charge-compensated filled skutterudites. Sci. Rep. 5:114641
    [Google Scholar]
  13. 13. 
    Zhang J, Song L, Pedersen SH, Yin H, Hung LT, Iversen BB. 2017. Discovery of high-performance low-cost n-type Mg3Sb2-based thermoelectric materials with multi-valley conduction bands. Nat. Commun. 8:113901
    [Google Scholar]
  14. 14. 
    Xing G, Sun J, Li Y, Fan X, Zheng W, Singh DJ. 2017. Electronic fitness function for screening semiconductors as thermoelectric materials. Phys. Rev. Mater. 1:6065405
    [Google Scholar]
  15. 15. 
    Xi L, Yang J, Wu L, Yang J, Zhang W 2016. Band engineering and rational design of high-performance thermoelectric materials by first-principles. J. Mater. 2:2114–30
    [Google Scholar]
  16. 16. 
    Mori H, Usui H, Ochi M, Kuroki K. 2017. Temperature- and doping-dependent roles of valleys in the thermoelectric performance of SnSe: a first-principles study. Phys. Rev. B 96:8085113
    [Google Scholar]
  17. 17. 
    Xi L, Pan S, Li X, Xu Y, Ni J et al. 2018. Discovery of high-performance thermoelectric chalcogenides through reliable high-throughput material screening. J. Am. Chem. Soc. 140:3410785–93
    [Google Scholar]
  18. 18. 
    Kim S-G, Mazin II, Singh DJ. 1998. First-principles study of Zn-Sb thermoelectrics. Phys. Rev. B 57:116199–203
    [Google Scholar]
  19. 19. 
    Scheidemantel TJ, Ambrosch-Draxl C, Thonhauser T, Badding JV, Sofo JO. 2003. Transport coefficients from first-principles calculations. Phys. Rev. B 68:12125210
    [Google Scholar]
  20. 20. 
    Madsen GKH, Schwarz K, Blaha P, Singh DJ. 2003. Electronic structure and transport in type-I and type-VIII clathrates containing strontium, barium, and europium. Phys. Rev. B 68:12125212
    [Google Scholar]
  21. 21. 
    Madsen GKH, Singh DJ. 2006. BoltzTraP. A code for calculating band-structure dependent quantities. Comput. Phys. Commun. 175:167–71
    [Google Scholar]
  22. 22. 
    Pizzi G, Volja D, Kozinsky B, Fornari M, Marzari N. 2014. BoltzWann: a code for the evaluation of thermoelectric and electronic transport properties with a maximally-localized Wannier functions basis. Comput. Phys. Commun. 185:1422–29
    [Google Scholar]
  23. 23. 
    Parker D, Singh DJ. 2010. High-temperature thermoelectric performance of heavily doped PbSe. Phys. Rev. B 82:3035204
    [Google Scholar]
  24. 24. 
    Wang H, Pei Y, LaLonde AD, Snyder GJ. 2011. Heavily doped p-type PbSe with high thermoelectric performance: an alternative for PbTe. Adv. Mater. 23:111366–70
    [Google Scholar]
  25. 25. 
    Ziman JM. 2001. Electrons and Phonons Oxford, UK: Oxford Univ. Press
  26. 26. 
    Jain A, Shin Y, Persson KA. 2016. Computational predictions of energy materials using density functional theory. Nat. Rev. Mater. 1:115004
    [Google Scholar]
  27. 27. 
    Yang J, Li H, Wu T, Zhang W, Chen L, Yang J 2008. Evaluation of half-Heusler compounds as thermoelectric materials based on the calculated electrical transport properties. Adv. Funct. Mater. 18:192880–88
    [Google Scholar]
  28. 28. 
    Bhattacharya S, Madsen GKH. 2015. High-throughput exploration of alloying as design strategy for thermoelectrics. Phys. Rev. B 92:8085205
    [Google Scholar]
  29. 29. 
    Snyder GJ, Toberer ES. 2008. Complex thermoelectric materials. Nat. Mater. 7:2105–14
    [Google Scholar]
  30. 30. 
    Kaasbjerg K, Thygesen KS, Jacobsen KW. 2012. Phonon-limited mobility in n-type single-layer MoS2 from first principles. Phys. Rev. B 85:11115317
    [Google Scholar]
  31. 31. 
    Wang Z, Wang S, Obukhov S, Vast N, Sjakste J et al. 2011. Thermoelectric transport properties of silicon: toward an ab initio approach. Phys. Rev. B 83:20205208
    [Google Scholar]
  32. 32. 
    Wang X, Askarpour V, Maassen J, Lundstrom M. 2018. On the calculation of Lorenz numbers for complex thermoelectric materials. J. Appl. Phys. 123:5055104
    [Google Scholar]
  33. 33. 
    Yoder PD, Natoli VD, Martin RM. 1993. Ab initio analysis of the electron-phonon interaction in silicon. J. Appl. Phys. 73:94378–83
    [Google Scholar]
  34. 34. 
    Xu B, Verstraete MJ. 2014. First principles explanation of the positive Seebeck coefficient of lithium. Phys. Rev. Lett. 112:19196603
    [Google Scholar]
  35. 35. 
    Bernardi M. 2016. First-principles dynamics of electrons and phonons. Eur. Phys. J. B 89:239
    [Google Scholar]
  36. 36. 
    Giustino F, Cohen ML, Louie SG. 2007. Electron-phonon interaction using Wannier functions. Phys. Rev. B 76:16165108
    [Google Scholar]
  37. 37. 
    Noffsinger J, Giustino F, Malone BD, Park C-H, Louie SG, Cohen ML. 2010. EPW: a program for calculating the electron-phonon coupling using maximally localized Wannier functions. Comput. Phys. Commun. 181:122140–48
    [Google Scholar]
  38. 38. 
    Zhou J-J, Park J, Lu I-T, Maliyov I, Tong X, Bernardi M. 2020. Perturbo: a software package for ab initio electron-phonon interactions, charge transport and ultrafast dynamics. arXiv:2002.02045 [cond-mat.mtrl-sci]
  39. 39. 
    Qiu B, Tian Z, Vallabhaneni A, Liao B, Mendoza JM et al. 2015. First-principles simulation of electron mean-free-path spectra and thermoelectric properties in silicon. EPL 109:557006
    [Google Scholar]
  40. 40. 
    Park C-HH, Bonini N, Sohier T, Samsonidze G, Kozinsky B et al. 2014. Electron-phonon interactions and the intrinsic electrical resistivity of graphene. Nano Lett 14:31113–19
    [Google Scholar]
  41. 41. 
    Wright AD, Verdi C, Milot RL, Eperon GE, Pérez-Osorio MA et al. 2016. Electron-phonon coupling in hybrid lead halide perovskites. Nat. Commun. 7:11755
    [Google Scholar]
  42. 42. 
    Liu T-H, Zhou J, Liao B, Singh DJ, Chen G. 2017. First-principles mode-by-mode analysis for electron-phonon scattering channels and mean free path spectra in GaAs. Phys. Rev. B 95:75206
    [Google Scholar]
  43. 43. 
    Vitale V, Pizzi G, Marrazzo A, Yates JR, Marzari N, Mostofi AA. 2020. Automated high-throughput Wannierisation. NPJ Comput. Mater. 6:166
    [Google Scholar]
  44. 44. 
    Samsonidze G, Kozinsky B. 2015. Accelerated screening of thermoelectric materials by first-principles computations of electron-phonon scattering. arXiv:1511.08115 [cond-mat.mtrl-sci]
  45. 45. 
    Samsonidze G, Kozinsky B. 2018. Accelerated screening of thermoelectric materials by first-principles computations of electron-phonon scattering. Adv. Energy Mater. 8:201800246
    [Google Scholar]
  46. 46. 
    Bang S, Kim J, Wee D, Samsonidze G, Kozinsky B. 2018. Estimation of electron-phonon coupling via moving least squares averaging: a method for fast-screening potential thermoelectric materials. Mater. Today Phys. 6:22–30
    [Google Scholar]
  47. 47. 
    Wee D, Kim J, Bang S, Samsonidze G, Kozinsky B. 2019. Quantification of uncertainties in thermoelectric properties of materials from a first-principles prediction method: an approach based on Gaussian process regression. Phys. Rev. Mater. 3:3033803
    [Google Scholar]
  48. 48. 
    Deng T, Wu G, Sullivan MB, Wong ZM, Hippalgaonkar K et al. 2020. EPIC STAR: a reliable and efficient approach for phonon- and impurity-limited charge transport calculations. NPJ Comput. Mater. 6:146
    [Google Scholar]
  49. 49. 
    Li X, Zhang Z, Xi J, Singh DJ, Sheng Y et al. 2021. TransOpt. A code to solve electrical transport properties of semiconductors in constant electron-phonon coupling approximation. Comput. Mater. Sci. 186:110074
    [Google Scholar]
  50. 50. 
    Mao J, Shuai J, Song S, Wu Y, Dally R et al. 2017. Manipulation of ionized impurity scattering for achieving high thermoelectric performance in n-type Mg3Sb2-based materials. PNAS 114:4010548–53
    [Google Scholar]
  51. 51. 
    Restrepo OD, Varga K, Pantelides ST. 2009. First-principles calculations of electron mobilities in silicon: phonon and coulomb scattering. Appl. Phys. Lett. 94:21212103
    [Google Scholar]
  52. 52. 
    He X, Singh DJ, Boon-on P, Lee M-W, Zhang L. 2018. Dielectric behavior as a screen in rational searches for electronic materials: metal pnictide sulfosalts. J. Am. Chem. Soc. 140:5118058–65
    [Google Scholar]
  53. 53. 
    Lee S, Hippalgaonkar K, Yang F, Hong J, Ko C et al. 2017. Anomalously low electronic thermal conductivity in metallic vanadium dioxide. Science 355:6323371–74
    [Google Scholar]
  54. 54. 
    Yao M, Zebarjadi M, Opeil CP. 2017. Experimental determination of phonon thermal conductivity and Lorenz ratio of single crystal metals: Al, Cu, and Zn. J. Appl. Phys. 122:13135111
    [Google Scholar]
  55. 55. 
    Flage-Larsen E, Prytz Ø. 2011. The Lorenz function: its properties at optimum thermoelectric figure-of-merit. Appl. Phys. Lett. 99:20202108
    [Google Scholar]
  56. 56. 
    Kumar GS, Prasad G, Pohl RO. 1993. Experimental determinations of the Lorenz number. J. Mater. Sci. 28:164261–72
    [Google Scholar]
  57. 57. 
    Luo Z, Tian J, Huang S, Srinivasan M, Maassen J et al. 2018. Large enhancement of thermal conductivity and Lorenz number in topological insulator thin films. ACS Nano 12:21120–27
    [Google Scholar]
  58. 58. 
    Upadhyaya M, Boyle CJ, Venkataraman D, Aksamija Z. 2019. Effects of disorder on thermoelectric properties of semiconducting polymers. Sci. Rep. 9:15820
    [Google Scholar]
  59. 59. 
    Lu N, Li L, Gao N, Liu M. 2016. Understanding electrical-thermal transport characteristics of organic semiconductors: violation of Wiedemann-Franz law. J. Appl. Phys. 120:19195108
    [Google Scholar]
  60. 60. 
    Poudel B, Hao Q, Ma Y, Lan Y, Minnich A et al. 2008. High-thermoelectric performance of nanostructured bismuth antimony telluride bulk alloys. Science 320:5876634–38
    [Google Scholar]
  61. 61. 
    Wan C, Wang Y, Wang N, Norimatsu W, Kusunoki M, Koumoto K. 2010. Development of novel thermoelectric materials by reduction of lattice thermal conductivity. Sci. Technol. Adv. Mater. 11:4044306
    [Google Scholar]
  62. 62. 
    Ahmad S, Mahanti SD. 2010. Energy and temperature dependence of relaxation time and Wiedemann-Franz law on PbTe. Phys. Rev. B 81:16165203
    [Google Scholar]
  63. 63. 
    Putatunda A, Singh DJ. 2019. Lorenz number in relation to estimates based on the Seebeck coefficient. Mater. Today Phys. 8:49–55
    [Google Scholar]
  64. 64. 
    McKinney RW, Gorai P, Stevanović V, Toberer ES. 2017. Search for new thermoelectric materials with low Lorenz number. J. Mater. Chem. A 5:3317302–11
    [Google Scholar]
  65. 65. 
    Slack GA. 1973. Nonmetallic crystals with high thermal conductivity. J. Phys. Chem. Solids 34:2321–35
    [Google Scholar]
  66. 66. 
    Nolas GS, Morelli DT, Tritt TM. 1999. Skutterudites: a phonon-glass-electron crystal approach to advanced thermoelectric energy conversion applications. Annu. Rev. Mater. Sci. 29:89–116
    [Google Scholar]
  67. 67. 
    He J, Tritt TM 2017. Advances in thermoelectric materials research: looking back and moving forward. Science 357:6358eeak9997
    [Google Scholar]
  68. 68. 
    Singh DJ, Terasaki I. 2008. Nanostructuring and more. Nat. Mater. 7:616–17
    [Google Scholar]
  69. 69. 
    Callaway J. 1959. Model for lattice thermal conductivity at low temperatures. Phys. Rev. 113:41046–51
    [Google Scholar]
  70. 70. 
    Klemens PG. 1951. The thermal conductivity of dielectric solids at low temperatures (theoretical). Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. 2081092:108–33
    [Google Scholar]
  71. 71. 
    Li R, Li X, Xi L, Yang J, Singh DJ, Zhang W. 2019. High-throughput screening for advanced thermoelectric materials: diamond-like ABX2 compounds. ACS Appl. Mater. Interfaces 11:2824859–66
    [Google Scholar]
  72. 72. 
    Nath P, Plata JJ, Usanmaz D, Toher C, Fornari M et al. 2017. High throughput combinatorial method for fast and robust prediction of lattice thermal conductivity. Scr. Mater. 129:88–93
    [Google Scholar]
  73. 73. 
    Broido DA, Malorny M, Birner G, Mingo N, Stewart DA. 2007. Intrinsic lattice thermal conductivity of semiconductors from first principles. Appl. Phys. Lett. 91:23231922
    [Google Scholar]
  74. 74. 
    Baroni S, De Gironcoli S, Dal Corso A, Giannozzi P 2001. Phonons and related crystal properties from density-functional perturbation theory. Rev. Mod. Phys. 73:2515–62
    [Google Scholar]
  75. 75. 
    Togo A, Chaput L, Tanaka I. 2015. Distributions of phonon lifetimes in Brillouin zones. Phys. Rev. B 91:9094306
    [Google Scholar]
  76. 76. 
    Hellman O, Abrikosov IA. 2013. Temperature-dependent effective third-order interatomic force constants from first principles. Phys. Rev. B 88:14144301
    [Google Scholar]
  77. 77. 
    Feng T, Lindsay L, Ruan X. 2017. Four-phonon scattering significantly reduces intrinsic thermal conductivity of solids. Phys. Rev. B 96:16161201
    [Google Scholar]
  78. 78. 
    Kang JS, Li M, Wu H, Nguyen H, Hu Y. 2018. Experimental observation of high thermal conductivity in boron arsenide. Science 361:6402575–78
    [Google Scholar]
  79. 79. 
    Li W, Carrete J, Katcho NA, Mingo N 2014. ShengBTE: a solver of the Boltzmann transport equation for phonons. Comput. Phys. Commun 185:61747–58
    [Google Scholar]
  80. 80. 
    Carrete J, Vermeersch B, Katre A, van Roekeghem A, Wang T et al. 2017. almaBTE: a solver of the space-time dependent Boltzmann transport equation for phonons in structured materials. Comput. Phys. Commun. 220:351–62
    [Google Scholar]
  81. 81. 
    Plata JJ, Nath P, Usanmaz D, Carrete J, Toher C et al. 2017. An efficient and accurate framework for calculating lattice thermal conductivity of solids: AFLOW—AAPL Automatic Anharmonic Phonon Library. NPJ Comput. Mater. 3:145
    [Google Scholar]
  82. 82. 
    Ravichandran NK, Broido D. 2018. Unified first-principles theory of thermal properties of insulators. Phys. Rev. B 98:8085205
    [Google Scholar]
  83. 83. 
    Simoncelli M, Marzari N, Mauri F. 2019. Unified theory of thermal transport in crystals and glasses. Nat. Phys. 15:8809–13
    [Google Scholar]
  84. 84. 
    Cepellotti A, Kozinsky B. 2021. Interband tunneling effects on materials transport properties using the first principles Wigner distribution. arXiv:2004.07358 [cond-mat.mtrl-sci]
  85. 85. 
    Garg J, Bonini N, Kozinsky B, Marzari N. 2011. Role of disorder and anharmonicity in the thermal conductivity of silicon-germanium alloys: a first-principles study. Phys. Rev. Lett. 106:4045901
    [Google Scholar]
  86. 86. 
    Liu H, Shi X, Xu F, Zhang L, Zhang W et al. 2012. Copper ion liquid-like thermoelectrics. Nat. Mater. 11:5422–25
    [Google Scholar]
  87. 87. 
    Lampin E, Nguyen QH, Francioso PA, Cleri F. 2012. Thermal boundary resistance at silicon-silica interfaces by molecular dynamics simulations. Appl. Phys. Lett. 100:13131906
    [Google Scholar]
  88. 88. 
    Ladd AJC, Moran B, Hoover WG. 1986. Lattice thermal conductivity: a comparison of molecular dynamics and anharmonic lattice dynamics. Phys. Rev. B 34:85058
    [Google Scholar]
  89. 89. 
    Fugallo G, Colombo L. 2018. Calculating lattice thermal conductivity: a synopsis. Phys. Scr. 93:4043002
    [Google Scholar]
  90. 90. 
    Zuo Y, Chen C, Li X, Deng Z, Chen Y et al. 2020. Performance and cost assessment of machine learning interatomic potentials. J. Phys. Chem. A 124:4731–45
    [Google Scholar]
  91. 91. 
    Vandermause J, Torrisi SB, Batzner S, Xie Y, Sun L et al. 2020. On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events. NPJ Comput. Mater. 6:120
    [Google Scholar]
  92. 92. 
    Podryabinkin EV, Shapeev AV. 2017. Active learning of linearly parametrized interatomic potentials. Comput. Mater. Sci. 140:171–80
    [Google Scholar]
  93. 93. 
    Xie Y, Vandermause J, Sun L, Cepellotti A, Kozinsky B. 2021. Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanine. NPJ Comput. Mater. 7:40
    [Google Scholar]
  94. 94. 
    Sosso GC, Deringer VL, Elliott SR, Csányi G. 2018. Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials. Mol. Simul. 44:11866–80
    [Google Scholar]
  95. 95. 
    Minamitani E, Ogura M, Watanabe S. 2019. Simulating lattice thermal conductivity in semiconducting materials using high-dimensional neural network potential. Appl. Phys. Express 12:9095001
    [Google Scholar]
  96. 96. 
    Mortazavi B, Podryabinkin EV, Novikov IS, Rabczuk T, Zhuang X, Shapeev AV. 2021. Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: a MTP/ShengBTE solution. Comput. Phys. Commun. 258:107583
    [Google Scholar]
  97. 97. 
    Liao B, Qiu B, Zhou J, Huberman S, Esfarjani K, Chen G. 2015. Significant reduction of lattice thermal conductivity by the electron-phonon interaction in silicon with high carrier concentrations: a first-principles study. Phys. Rev. Lett. 114:11115901
    [Google Scholar]
  98. 98. 
    Protik NH, Kozinsky B. 2020. Electron-phonon drag enhancement of transport properties from a fully coupled ab initio Boltzmann formalism. Phys. Rev. B 102:245202
    [Google Scholar]
  99. 99. 
    Curtarolo S, Hart GLW, Nardelli MB, Mingo N, Sanvito S, Levy O. 2013. The high-throughput highway to computational materials design. Nat. Mater. 12:3191–201
    [Google Scholar]
  100. 100. 
    Akbarzadeh AR, Ozoliņš V, Wolverton C. 2007. First-principles determination of multicomponent hydride phase diagrams: application to the Li-Mg-N-H system. Adv. Mater. 19:203233–39
    [Google Scholar]
  101. 101. 
    Samsonidze G, Kozinsky B. 2013. Materials for thermoelectric energy conversion. US Patent 10:439121
    [Google Scholar]
  102. 102. 
    Zagorac D, Muller H, Ruehl S, Zagorac J, Rehme S. 2019. Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features. J. Appl. Crystallogr. 52:5918–25
    [Google Scholar]
  103. 103. 
    Graulis S, Chateigner D, Downs RT, Yokochi AFT, Quirós M et al. 2009. Crystallography Open Database—an open-access collection of crystal structures. J. Appl. Crystallogr. 42:4726–29
    [Google Scholar]
  104. 104. 
    Curtarolo S, Setyawan W, Hart GLW, Jahnatek M, Chepulskii RV et al. 2012. AFLOW: an automatic framework for high-throughput materials discovery. Comput. Mater. Sci. 58:218–26
    [Google Scholar]
  105. 105. 
    Ong SP, Richards WD, Jain A, Hautier G, Kocher M et al. 2013. Python Materials Genomics (pymatgen): a robust, open-source python library for materials analysis. Comput. Mater. Sci. 68:314–19
    [Google Scholar]
  106. 106. 
    Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C. 2013. Materials design and discovery with high-throughput density functional theory: the Open Quantum Materials Database (OQMD). JOM 65:111501–9
    [Google Scholar]
  107. 107. 
    Opahle I, Madsen GKH, Drautz R. 2012. High throughput density functional investigations of the stability, electronic structure and thermoelectric properties of binary silicides. Phys. Chem. Chem. Phys. 14:4716197–202
    [Google Scholar]
  108. 108. 
    Oganov AR, Glass CW. 2006. Crystal structure prediction using ab initio evolutionary techniques: principles and applications. J. Chem. Phys. 124:24244704
    [Google Scholar]
  109. 109. 
    Wang Y, Lv J, Zhu L, Ma Y. 2012. CALYPSO: a method for crystal structure prediction. Comput. Phys. Commun. 183:102063–70
    [Google Scholar]
  110. 110. 
    Frenkel D, Smit B. 2002. Understanding Molecular Simulation: From Algorithms to Applications San Diego, CA: Acad. Press
  111. 111. 
    Artrith N, Morawietz T, Behler J. 2011. High-dimensional neural-network potentials for multicomponent systems: applications to zinc oxide. Phys. Rev. B 83:15153101
    [Google Scholar]
  112. 112. 
    Noé F, Tkatchenko A, Müller K-R, Clementi C. 2020. Machine learning for molecular simulation. Annu. Rev. Phys. Chem. 71:361–90
    [Google Scholar]
  113. 113. 
    Cococcioni M, de Gironcoli S. 2005. Linear response approach to the calculation of the effective interaction parameters in the LDA+U method. Phys. Rev. B. 71:3035105
    [Google Scholar]
  114. 114. 
    LeBlanc S. 2014. Thermoelectric generators: linking material properties and systems engineering for waste heat recovery applications. Sustain. Mater. Technol. 1:26–35
    [Google Scholar]
  115. 115. 
    Joshi G, He R, Engber M, Samsonidze G, Pantha T et al. 2014. NbFeSb-based p-type half-Heuslers for power generation applications. Energy Environ. Sci. 7:124070–76
    [Google Scholar]
  116. 116. 
    Joshi G, Yan X, Wang H, Liu W, Chen G, Ren Z 2011. Enhancement in thermoelectric figure-of-merit of an n-type half-Heusler compound by the nanocomposite approach. Adv. Energy Mater. 1:4643–47
    [Google Scholar]
  117. 117. 
    Wee D, Kozinsky B, Pavan B, Fornari M. 2012. Quasiharmonic vibrational properties of TiNiSn from ab initio phonons. J. Electron. Mater. 41:6977–83
    [Google Scholar]
  118. 118. 
    Madsen GKH. 2006. Automated search for new thermoelectric materials: the case of LiZnSb. J. Am. Chem. Soc. 128:3712140–46
    [Google Scholar]
  119. 119. 
    Toberer ES, May AF, Scanlon CJ, Snyder GJ. 2009. Thermoelectric properties of p-type LiZnSb: assessment of ab initio calculations. J. Appl. Phys. 105:6063701
    [Google Scholar]
  120. 120. 
    Chen W, Pöhls J-H, Hautier G, Broberg D, Bajaj S et al. 2016. Understanding thermoelectric properties from high-throughput calculations: trends, insights, and comparisons with experiment. J. Mater. Chem. C 4:204414–26
    [Google Scholar]
  121. 121. 
    Jain A, Ong SP, Hautier G, Chen W, Richards WD et al. 2013. Commentary: the Materials Project: a materials genome approach to accelerating materials innovation. APL Mater 1:1011002
    [Google Scholar]
  122. 122. 
    Zhu H, Hautier G, Aydemir U, Gibbs ZM, Li G et al. 2015. 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 3:4010554–65
    [Google Scholar]
  123. 123. 
    Young DP, Khalifah P, Cava RJ, Ramirez AP. 2000. Thermoelectric properties of pure and doped FeMSb (M = V,Nb). J. Appl. Phys. 87:1317–21
    [Google Scholar]
  124. 124. 
    Joshi G, Yang J, Engber M, Pantha T, Cleary M et al. 2014. NbFeSb-based half-Heusler thermoelectric materials and methods of fabrication and use. US Patent10 008:653
    [Google Scholar]
  125. 125. 
    Fu C, Zhu T, Pei Y, Xie H, Wang H et al. 2014. High band degeneracy contributes to high thermoelectric performance in p-type half-Heusler compounds. Adv. Energy Mater. 4:181400600
    [Google Scholar]
  126. 126. 
    Fu C, Zhu T, Liu Y, Xie H, Zhao X. 2015. Band engineering of high performance p-type FeNbSb based half-Heusler thermoelectric materials for figure of merit zT >1. Energy Environ. Sci. 8:1216–20
    [Google Scholar]
  127. 127. 
    Fu C, Bai S, Liu Y, Tang Y, Chen L et al. 2015. Realizing high figure of merit in heavy-band p-type half-Heusler thermoelectric materials. Nat. Commun. 6:18144
    [Google Scholar]
  128. 128. 
    Yu J, Fu C, Liu Y, Xia K, Aydemir U et al. 2018. Unique role of refractory Ta alloying in enhancing the figure of merit of NbFeSb thermoelectric materials. Adv. Energy Mater. 8:11701313
    [Google Scholar]
  129. 129. 
    Zhu H, Mao J, Li Y, Sun J, Wang Y et al. 2019. Discovery of TaFeSb-based half-Heuslers with high thermoelectric performance. Nat. Commun. 10:1270
    [Google Scholar]
  130. 130. 
    Shen J, Fan L, Hu C, Zhu T, Xin J et al. 2019. Enhanced thermoelectric performance in the n-type NbFeSb half-Heusler compound with heavy element Ir doping. Mater. Today Phys. 8:62–70
    [Google Scholar]
  131. 131. 
    Ponnambalam V, Zhang B, Tritt TM, Poon SJ. 2007. Thermoelectric properties of half-Heusler bismuthides ZrCo1−x NixBi (x = 0.0 to 0.1). J. Electron. Mater. 36:7732–35
    [Google Scholar]
  132. 132. 
    Zhu H, He R, Mao J, Zhu Q, Li C et al. 2018. Discovery of ZrCoBi based half Heuslers with high thermoelectric conversion efficiency. Nat. Commun. 9:12497
    [Google Scholar]
  133. 133. 
    Ono Y, Inayama S, Adachi H, Kajitani T. 2006. Thermoelectric properties of NbCoSn-based half-Heusler alloys. 2006 25th International Conference on Thermoelectrics124–27 Piscataway, NJ: IEEE
    [Google Scholar]
  134. 134. 
    He R, Huang L, Wang Y, Samsonidze G, Kozinsky B et al. 2016. Enhanced thermoelectric properties of n-type NbCoSn half-Heusler by improving phase purity. APL Mater 4:10104804
    [Google Scholar]
  135. 135. 
    Li S, Zhu H, Mao J, Feng Z, Li X et al. 2019. n-Type TaCoSn-based half-Heuslers as promising thermoelectric materials. ACS Appl. Mater. Interfaces 11:4441321–29
    [Google Scholar]
  136. 136. 
    Bhattacharya S, Madsen GKH. 2016. A novel p-type half-Heusler from high-throughput transport and defect calculations. J. Mater. Chem. C 4:4711261–68
    [Google Scholar]
  137. 137. 
    Wei J, Yang L, Ma Z, Song P, Zhang M et al. 2020. Review of current high-ZT thermoelectric materials. J. Mater. Sci. 55:12642–704
    [Google Scholar]
  138. 138. 
    Yan J, Gorai P, Ortiz B, Miller S, Barnett SA et al. 2015. Material descriptors for predicting thermoelectric performance. Energy Environ. Sci. 8:3983–94
    [Google Scholar]
  139. 139. 
    Ortiz BR, Gorai P, Krishna L, Mow R, Lopez A et al. 2017. Potential for high thermoelectric performance in n-type Zintl compounds: a case study of Ba doped KAlSb4. J. Mater. Chem. A 5:84036–46
    [Google Scholar]
  140. 140. 
    Ortiz BR, Gorai P, Stevanović V, Toberer ES. 2017. Thermoelectric performance and defect chemistry in n-type Zintl KGaSb4. Chem. Mater. 29:104523–34
    [Google Scholar]
  141. 141. 
    Feng Z, Fu Y, Putatunda A, Zhang Y, Singh DJ. 2019. Electronic structure as a guide in screening for potential thermoelectrics: demonstration for half-Heusler compounds. Phys. Rev. B 100:8085202
    [Google Scholar]
  142. 142. 
    Carrete J, Li W, Mingo N, Wang S, Curtarolo S 2014. Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling. Phys. Rev. X 4:1011019
    [Google Scholar]
  143. 143. 
    Juneja R, Yumnam G, Satsangi S, Singh AK. 2019. Coupling the high-throughput property map to machine learning for predicting lattice thermal conductivity. Chem. Mater. 31:145145–51
    [Google Scholar]
  144. 144. 
    Anand S, Wood M, Xia Y, Wolverton C, Snyder GJ. 2019. Double half-Heuslers. Joule 3:51226–38
    [Google Scholar]
  145. 145. 
    Zhou F, Nielson W, Xia Y, Ozoliņš V. 2014. Lattice anharmonicity and thermal conductivity from compressive sensing of first-principles calculations. Phys. Rev. Lett. 113:18185501
    [Google Scholar]
  146. 146. 
    Isaacs EB, Lu GM, Wolverton C. 2020. Inverse design of ultralow lattice thermal conductivity materials via materials database screening of lone pair cation coordination environment. J. Phys. Chem. Lett. 11:145577–83
    [Google Scholar]
  147. 147. 
    Jain A, Ong SP, Chen W, Medasani B, Qu X et al. 2015. FireWorks: a dynamic workflow system designed for high-throughput applications. Concurr. Comput. Pract. Exp. 27:175037–59
    [Google Scholar]
  148. 148. 
    Pizzi G, Cepellotti A, Sabatini R, Marzari N, Kozinsky B. 2016. AiiDA: automated interactive infra-structure and database for computational science. Comput. Mater. Sci. 111:218–30
    [Google Scholar]
  149. 149. 
    Mounet N, Gibertini M, Schwaller P, Campi D, Merkys A et al. 2018. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nat. Nanotechnol. 13:3246–52
    [Google Scholar]
  150. 150. 
    Sparks TD, Gaultois MW, Oliynyk A, Brgoch J, Meredig B. 2016. Data mining our way to the next generation of thermoelectrics. Scr. Mater. 111:10–15
    [Google Scholar]
  151. 151. 
    Koumoto K, Koduka H, Seo WS. 2001. Thermoelectric properties of single crystal CuAlO2 with a layered structure. J. Mater. Chem. 11:2251–52
    [Google Scholar]
  152. 152. 
    Zhang Q, Song Q, Wang X, Sun J, Zhu Q et al. 2018. Deep defect level engineering: a strategy of optimizing the carrier concentration for high thermoelectric performance. Energy Environ. Sci. 11:4933–40
    [Google Scholar]
  153. 153. 
    Ravich YI, Efimova BA, Tamarchenko VI. 1971. Scattering of current carriers and transport phenomena in lead chalcogenides II. Experiment. Phys. Status Solidi 43:2453–69
    [Google Scholar]
  154. 154. 
    Singh DJ. 2010. Doping-dependent thermopower of PbTe from Boltzmann transport calculations. Phys. Rev. B 81:19195217
    [Google Scholar]
  155. 155. 
    Fang T, Li X, Hu C, Zhang Q, Yang J et al. 2019. Complex band structures and lattice dynamics of Bi2Te3-based compounds and solid solutions. Adv. Funct. Mater. 29:281900677
    [Google Scholar]
  156. 156. 
    Shi H, Parker D, Du MH, Singh DJ. 2015. Connecting thermoelectric performance and topological-insulator behavior: Bi2Te3 and Bi2Te2Se from first principles. Phys. Rev. Appl. 3:1014004
    [Google Scholar]
  157. 157. 
    Tan G, Shi F, Hao S, Zhao L-D, Chi H et al. 2016. Non-equilibrium processing leads to record high thermoelectric figure of merit in PbTe-SrTe. Nat. Commun. 7:112167
    [Google Scholar]
  158. 158. 
    Ravich YI, Efimova BA, Smirnov IA. 1970. Semiconducting Lead Chalcogenides New York: Plenum Press
  159. 159. 
    Chen Z-G, Han G, Yang L, Cheng L, Zou J. 2012. Nanostructured thermoelectric materials: current research and future challenge. Prog. Nat. Sci. Mater. Int. 22:6535–49
    [Google Scholar]
  160. 160. 
    Aketo D, Shiga T, Shiomi J. 2014. Scaling laws of cumulative thermal conductivity for short and long phonon mean free paths. Appl. Phys. Lett. 105:13131901
    [Google Scholar]
  161. 161. 
    Minnich AJ, Johnson JA, Schmidt AJ, Esfarjani K, Dresselhaus MS et al. 2011. Thermal conductivity spectroscopy technique to measure phonon mean free paths. Phys. Rev. Lett. 107:9095901
    [Google Scholar]
  162. 162. 
    Stöhr H, Klemm W. 1939. Über Zweistoffsysteme mit Germanium. I. Germanium/Aluminium, Germanium/Zinn und Germanium/Silicium. Z. Anorg. Allg. Chem. 241:4305–23
    [Google Scholar]
  163. 163. 
    Abeles B. 1963. Lattice thermal conductivity of disordered semiconductor alloys at high temperatures. Phys. Rev. 131:190611
    [Google Scholar]
/content/journals/10.1146/annurev-matsci-100520-015716
Loading
/content/journals/10.1146/annurev-matsci-100520-015716
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error