Review Article | Published:

The high-throughput highway to computational materials design

Nature Materials volume 12, pages 191201 (2013) | Download Citation

Abstract

High-throughput computational materials design is an emerging area of materials science. By combining advanced thermodynamic and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyse enormous data repositories for the discovery of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and opportunities that lie ahead.

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References

  1. 1.

    Electronic Structure: Basic Theory and Practical Methods (Cambridge Univ. Press, 2004).

  2. 2.

    in Solid State Physics (eds Ehrenreich, H. & Turnbull, D) Vol. 47, 33–176 (Wiley, 1994).

  3. 3.

    , , , & Predicting crystal structures with data mining of quantum calculations. Phys. Rev. Lett. 91, 135503 (2003).

  4. 4.

    Edison, the Man and His Work (Knopf, 1930).

  5. 5.

    The photochemistry of the future. Science 36, 385–394 (1912).

  6. 6.

    et al. Identification of cathode materials for lithium batteries guided by first-principles calculations. Nature 392, 694–696 (1998).

  7. 7.

    et al. Combined electronic structure and evolutionary search approach to materials design. Phys. Rev. Lett. 88, 255506 (2002).

  8. 8.

    & Predictions of new crystalline states for assemblies of nanoparticles: perovskite analogues and 3-D arrays of self-assembled nanowires. Nano Lett. 3, 1183–1186 (2003).

  9. 9.

    , & Accuracy of ab initio methods in predicting the crystal structures of metals: review of 80 binary alloys. Calphad 29, 163–211 (2005).

  10. 10.

    , , & Predicting crystal structure by merging data mining with quantum mechanics. Nature Mater. 5, 641–646 (2006).

  11. 11.

    et al. Parteo-optimal alloys. Appl. Phys. Lett. 83, 4527–4529 (2003).

  12. 12.

    et al. Toward computational screening in heterogeneous catalysis: Pareto-optimal methanation catalysts, J. Catal. 239, 501–506 (2006).

  13. 13.

    , & Uncovering compounds by synergy of cluster expansion and high-throughput methods. J. Am. Chem. Soc. 132, 4830–4833 (2010).

  14. 14.

    , , & New face of rhodium alloys: revealing ordered structures from first principles. J. Am. Chem. Soc. 132, 833–837 (2010).

  15. 15.

    , , , & Assessing the thermoelectric properties of sintered compounds via high-throughput ab-initio calculations. Phys. Rev. X 1, 021012 (2011).

  16. 16.

    , , , & A search model for topological insulators with high-throughput robustness descriptors. Nature Mater. 11, 614–619 (2012).

  17. 17.

    et al. A combinatorial approach to materials discovery. Science 268, 1738–1740 (1995).

  18. 18.

    et al. Identification of novel compositions of ferromagnetic shape-memory alloys using composition spreads. Nature Mater. 2, 180–184 (2003).

  19. 19.

    , & Combinatorial solid-state chemistry of inorganic materials. Nature Mater. 3, 429–438 (2004).

  20. 20.

    et al. Glossary of terms used in combinatorial chemistry. Pure Appl. Chem. 71, 2349–2365 (1999).

  21. 21.

    in Hochdurchsatz-Untersuchungen, in Winnacker-Küchler Chemische Technik 5th edn (eds Dittmeyer, R., Keim, W., Kreysa, G. & Oberholz, A.) Ch. 5, 549–585 (Wiley, 2004).

  22. 22.

    , & Combinatorial and high-throughput materials science. Angew. Chem. Int. Ed. 46, 6016–6067 (2007).

  23. 23.

    & High-throughput electronic band structure calculations: challenges and tools. Comp. Mater. Sci. 49, 299–312 (2010).

  24. 24.

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

  25. 25.

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

  26. 26.

    et al. Combinatorial discovery of metal co-catalysts for the carbonylation of phenol. Appl. Catal. A 254, 5–25 (2003).

  27. 27.

    , , & Recharging lithium battery research with first-principles methods. Mater. Res. Soc. Bull. 36, 185–191 (2011).

  28. 28.

    Opportunities and challenges for first-principles materials design and applications to Li battery materials. Mater. Res. Soc. Bull. 35, 693–701 (2010).

  29. 29.

    & Prediction of new crystal structure phases in metal borides: a lithium monoboride analog to MgB2. Phys. Rev. B 73, 180501(R) (2006).

  30. 30.

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

  31. 31.

    , , & Evolutionary approach for determining first-principles hamiltonians. Nature Mater. 4, 391–394 (2005).

  32. 32.

    Structure maps revisited. J. Phys. Condens. Matter 15, V13–V16 (2003).

  33. 33.

    , & Automatic construction, implementation and assessment of Pettifor maps. J. Phys. Condens. Matter 15, 4361–4369 (2003).

  34. 34.

    , & Structure maps for hcp metals from first-principles calculations. Phys. Rev. B 81, 174106 (2010).

  35. 35.

    , , , & Performance of neural networks in materials science. Mater. Sci. Technol. 25, 504–510 (2009).

  36. 36.

    & On the design, analysis, and characterization of materials using computational neural networks. Annu. Rev. Mater. Sci. 26, 223–277 (1996).

  37. 37.

    , , & Zeolite synthesis modelling with support vector machines: a combinatorial approach. Comb. Chem. High Throughput Screen. 10, 13–24 (2007).

  38. 38.

    , , & (eds) Binary Alloy Phase Diagrams (ASM, 1990).

  39. 39.

    et al. Crystal Impact, Pauling File. Inorganic Materials Database and Design System, Binaries Edition (ASM, 2003).

  40. 40.

    , , , & The TiPd2 compound studied by PAC with 181Ta and 111Cd probes. J. Alloys Compound 385, 53–58 (2004).

  41. 41.

    et al. Structure, bonding, and possible superhardness of CrB4. Phys. Rev. B 85, 144116 (2012).

  42. 42.

    et al. Possible routes for synthesis of new boron-rich FeB and Fe1−xCrxB4 compounds. Appl. Phys. Lett. 98, 081901 (2011).

  43. 43.

    , , , & Pressure-driven evolution of the covalent network in CaB6. Phys. Rev. Lett. 109, 075501 (2012).

  44. 44.

    et al. AFLOW: an automatic framework for high-throughput materials discovery. Comp. Mater. Sci. 58, 218–226 (2012).

  45. 45.

    & Ising model phase-diagram calculations in the fcc lattice with first- and second-neighbor interactions. Phys. Rev. B 25, 1759–1765 (1982).

  46. 46.

    , , & Ordering and magnetism in Fe-Co: dense sequence of ground-state structures. Phys. Rev. Lett. 93, 067202 (2004).

  47. 47.

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

  48. 48.

    & Generating derivative structures: Algorithm and applications. Phys. Rev. B 77, 224115 (2008).

  49. 49.

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

  50. 50.

    & Crystal structure prediction using ab initio evolutionary techniques: Principles and applications. J. Chem. Phys. 124, 244704 (2006).

  51. 51.

    , & Metastable high-pressure single-bonded phases of nitrogen predicted via genetic algorithm. Phys. Rev. B 77, 052103 (2008).

  52. 52.

    & Evolutionary search for superhard materials: Methodology and applications to forms of carbon and TiO2. Phys. Rev. B 84, 092103 (2011).

  53. 53.

    , , & Compressive sensing as a new paradigm in model building. Phys. Rev. B 87, 035125 (2013).

  54. 54.

    A chemical scale for crystal-structure maps. Solid State Commun. 51, 31–34 (1984).

  55. 55.

    , , , & Ordered structures in rhenium binary alloys from first-principles calculations. J. Am. Chem. Soc. 133, 158–163 (2011).

  56. 56.

    et al. Ordered structures and vibrational stabilization in rhutenium alloys from first principles calculations. Phys. Rev. B 84, 214110 (2011).

  57. 57.

    , & Guiding the experimental discovery of magnesium alloys. Phys. Rev. B 84, 084101 (2011).

  58. 58.

    , & Hafnium binary alloys from experiments and first principles. Acta Mater. 58, 2887–2897 (2010).

  59. 59.

    et al. Prediction and hydrogen-acceleration of ordering in iron-vanadium alloys. Phys. Rev. Lett. 108, 215503 (2012).

  60. 60.

    , , , & Solar cell efficiency tables (version 9). Prog. Photovoltaics: Res. Applications 5, 51–54 (1997).

  61. 61.

    , & Materials availability expands the opportunity for large-scale photovoltaics deployment. Environ. Sci. Technol. 43, 2072–2077 (2009).

  62. 62.

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

  63. 63.

    , , & The inorganic crystal structure data base. J. Chem. Inf. Comput. Sci. 23, 66–69 (1983).

  64. 64.

    et al. Computational screening of perovskite metal oxides for optimal solar light capture. Energy Environ. Sci. 5, 5814–5819 (2012).

  65. 65.

    et al. In silico screening of carbon-capture materials. Nature Mater. 11, 633–641 (2012).

  66. 66.

    & In silico screening of metal-organic frameworks in separation applications. Phys. Chem. Chem. Phys. 13, 10593–10616 (2011).

  67. 67.

    & Screening metal-organic frameworks by analysis of transient breakthrough of gas mixtures in a fixed bed adsorber. J. Phys. Chem. C 115, 12941–12950 (2011).

  68. 68.

    et al. Screening of metal-organic frameworks for carbon dioxide capture from flue gas using a combined experimental and modeling approach. J. Am. Chem. Soc. 131, 18198–18199 (2009).

  69. 69.

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

  70. 70.

    , & Large-scale screening of metal hydride mixtures for high-capacity hydrogen storage from first-principles calculations. J. Phys. Chem. C 112, 5258–5262 (2008).

  71. 71.

    , , & Potential of binary lithium magnesium nitride for hydrogen storage applications. J. Phys. Chem. C 111, 12129–12134 (2007).

  72. 72.

    et al. New scintillators discovered by high-throughput screening. Nuclear Inst. Methods Phys. Res. A 652, 247–250 (2011).

  73. 73.

    , & Data mining and accelerated electronic structure theory as a tool in the search for new functional materials. Comp. Mater. Sci. 44, 1042–1049 (2009).

  74. 74.

    The Electronic Structure Project;

  75. 75.

    , , & Comparative study of nonproportionality and electronic band structures features in scintillator materials. IEEE Trans. Nucl. Sci. 56, 2989–2996 (2009).

  76. 76.

    , , , & High-throughput combinatorial database of electronic band structures for inorganic scintillator materials. ACS Comb. Sci. 13, 382–390 (2011).

  77. 77.

    & Colloquium: Topological insulators. Rev. Mod. Phys. 82, 3045–3067 (2010).

  78. 78.

    et al. Half-Heusler ternary compounds as new multifunctional experimental platforms for topological quantum phenomena. Nature Mater. 9, 546–549 (2010).

  79. 79.

    , , & Screening for high-performance piezoelectrics using high-throughput density functional theory. Phys. Rev. B 84, 014103 (2011).

  80. 80.

    , , & Half-Heusler semiconductors as piezoelectrics. Phys. Rev. Lett. 109, 037602 (2012).

  81. 81.

    & The best thermoelectric. Proc. Natl Acad. Sci. USA 93, 7436–7439 (1996).

  82. 82.

    & Complex thermoelectric materials. Nature Mater. 7, 105–114 (2008).

  83. 83.

    Automated search for new thermoelectric materials: the case of LiZnSb. J. Am. Chem. Soc. 128, 12140–12146 (2006).

  84. 84.

    , , & Thermoelectric properties of p-type LiZnSb: Assessment of ab initio calculations. J. Appl. Phys. 105, 063701 (2009).

  85. 85.

    et al. Evaluation of half-Heusler compounds as thermoelectric materials based on the calculated electrical transport properties. Adv. Func. Mater. 18, 2880–2888 (2008).

  86. 86.

    in Solid State Physics Vol. 51 (ed. Ehrenreich, F. S. H.) 81–158 (Academic, 1998).

  87. 87.

    , , & Towards the computational design of solid catalysts. Nature Chem. 1, 37–46 (2009).

  88. 88.

    , , & Density functional theory insurface chemistry and catalysis. Proc. Natl Acad. Sci. USA 108, 937–943 (2011).

  89. 89.

    & First-principles-based Monte Carlo simulation of ethylene hydrogenation kinetics on Pd. J. Catal. 196, 241–252 (2000).

  90. 90.

    & Construction of a reaction coordinate and a microkinetic model for ethylene epoxidation on silver from DFT calculations and surface science experiments. J. Catal. 214, 200–212 (2003).

  91. 91.

    , & The steady state of heterogeneous catalysis studied by first-principles statistical mechanics. Phys. Rev. Lett. 93, 116105 (2004).

  92. 92.

    Ammonia synthesis from first-principles calculations. Science 307, 555–558 (2005).

  93. 93.

    Prediction of experimental methanol decomposition rates on platinum from first principles. Top. Catal. 37, 17–28 (2006).

  94. 94.

    et al. Exploring computational design of size-specific subnanometer clusters catalysts. Top. Catal. 55, 353–365 (2012).

  95. 95.

    & Toward efficient hydrogen production at surfaces. Science 312, 1322–1323 (2006).

  96. 96.

    , , , & Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nature Mater. 5, 909–913 (2006).

  97. 97.

    et al. Discovery of technical methanation catalysts based on computational screening. Top. Catal. 45, 9–13 (2007).

  98. 98.

    & Electrochemical dissolution of surface alloys in acids: Thermodynamic trends from first-principles calculations. Electrochimica Acta 52, 5829–5836 (2007).

  99. 99.

    & Catalysis: Bond control in surface reactions. Nature 461, 1223–1225 (2009).

  100. 100.

    et al. Scaling properties of adsorption energies for hydrogen-containing molecules on transition metal surfaces. Phys. Rev. Lett. 99, 016105 (2007).

  101. 101.

    et al. Universality in heterogeneous catalysis. J. Catal. 209, 275–278 (2002).

  102. 102.

    et al. The Brønsted-Evans-Polanyi relation and the volcano curve in heterogeneous catalysis. J. Catal. 224, 206–217 (2004).

  103. 103.

    , , & Heterogeneous Catalysis and Solid Catalysts. 1. Fundamentals Ch. 1, 457–481 (Wiley, 2011).

  104. 104.

    et al. High throughput experimental and theoretical predictive screening of materials — A comparative study of search strategies for new fuel cell anode catalysts. J. Phys. Chem. B 107, 11013–11021 (2003).

  105. 105.

    , & Selectivity driven design of bimetallic ethylene epoxidation catalysts from first principles. J. Catal. 224, 489–493 (2004).

  106. 106.

    & Alloy catalysts designed from first principles. Nature Mater. 3, 810–815 (2004).

  107. 107.

    et al. Identification of non-precious metal alloy catalysts for selective hydrogenation of acetylene. Science 320, 1320–1322 (2008).

  108. 108.

    et al. Alloys of platinum and early transition metals as oxygen reduction electrocatalysts. Nature Chem. 1, 552–556 (2009).

  109. 109.

    Materials challenges facing electrical energy storage. Mater. Res. Soc. Bull. 33, 411–419 (2008).

  110. 110.

    Nonaqueous liquid electrolytes for lithium-based rechargeable batteries. Chem. Rev. 104, 4303–4417 (2004).

  111. 111.

    et al. Exfoliated MoS2 nanocomposite as an anode material for lithium ion batteries. Chem. Mater. 22, 4522–4524 (2010).

  112. 112.

    , , & Electrochemical cycling reversibility of LiMoS2 using first-principles calculations. Appl. Phys. Lett. 100, 263901 (2012).

  113. 113.

    Lithium batteries and cathode materials. Chem. Rev. 104, 4271–4301 (2004).

  114. 114.

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

  115. 115.

    , , , & Finding nature's missing ternary oxide compounds using machine learning and density functional theory. Chem. Mater. 22, 3762–3767 (2010).

  116. 116.

    , , , & Hybrid density functional calculations of redox potentials and formation energies of transition metal compounds. Phys. Rev. B 82, 075122 (2010).

  117. 117.

    , , , & First-principles prediction of redox potentials in transition-metal compounds with LDA + U. Phys. Rev. B 70, 235121 (2004).

  118. 118.

    & Linear response approach to the calculation of the effective interaction parameters in the LDA + U method. Phys. Rev. B 71, 035105 (2005).

  119. 119.

    , & Oxidation energies of transition metal oxides within the GGA+U framework. Phys. Rev. B 73, 195107 (2006).

  120. 120.

    et al. Phosphates as lithium-ion battery cathodes: an evaluation based on high-throughput ab initio calculations. Chem. Mater. 23, 3495–3508 (2011).

  121. 121.

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

  122. 122.

    , , & Evaluation of tavorite-structured cathode materials for lithium-ion batteries using high-throughput computing. Chem. Mater. 23, 3854–3862 (2011).

  123. 123.

    , & First-principles calculations of electron mobilities in silicon: phonon and Coulomb scattering. Appl. Phys. Lett. 94, 212103 (2009).

  124. 124.

    & First-principles calculation of carrier-phonon scattering in n-type Si1−xGex alloys. Phys. Rev. B 78, 035202 (2008).

  125. 125.

    , , , & Intrinsic lattice thermal conductivity of semiconductors from first principles. Appl. Phys. Lett. 91, 231922 (2007).

  126. 126.

    , , & Ab initio theory of the lattice thermal conductivity in diamond. Phys. Rev. B 80, 125203 (2009).

  127. 127.

    & Intrinsic phonon relaxation times from first-principles studies of the thermal conductivities of Si and Ge. Phys. Rev. B 81, 085205 (2010).

  128. 128.

    , , & Role of light and heavy embedded nanoparticles on the thermal conductivity of SiGe alloys. Phys. Rev. B 84, 125426 (2011).

  129. 129.

    , & Thermal conductivity of half-Heusler compounds from first-principles calculations. Phys. Rev. B 84, 104302 (2011).

  130. 130.

    , , & Role of disorder and anharmonicity in the thermal conductivity of silicon-germanium alloys: A first-principles study. Phys. Rev. Lett. 106, 045901 (2011).

  131. 131.

    , & Thermal conductivity and large isotope effect in GaN from first principles. Phys. Rev. Lett. 109, 095901 (2012).

  132. 132.

    , & GIBBS: isothermal-isobaric thermodynamics of solids from energy curves using a quasi-harmonic Debye model. Computer Phys. Commun. 158, 57–72 (2004).

  133. 133.

    , , & Ab initio transport properties of nanostructures from maximally localized Wannier functions. Phys. Rev. B 69, 035108 (2004).

  134. 134.

    Magnetism and Magnetic Materials Ch. 1, 10–23 (Oxford Univ. Press, 2009).

  135. 135.

    , & Simple rules for the understanding of Heusler compounds. Prog. Solid State Chem. 39, 1–50 (2011).

  136. 136.

    & Managing the scarcity of chemical elements. Nature Mater. 10, 158–161 (2011).

  137. 137.

    & Giant tunnel magnetoresistance in magnetic tunnel junctions with a crystalline MgO(001) barrier. J. Phys. D 40, R337–R354 (2007).

  138. 138.

    et al. Tunable multifunctional topological insulators in ternary Heusler compounds. Nature Mater. 9, 541–545 (2010).

  139. 139.

    , , & Sorting stable versus unstable hypothetical compounds: The case of multi-functional ABX half-Heusler filled tetrahedral structures. Adv. Func. Mater. 22, 1425–1435 (2012).

  140. 140.

    Comparative ab initio study of half-Heusler compounds for optoelectronic applications. Phys. Rev. B 82, 125210 (2010).

  141. 141.

    Surface stress and the chemical equilibrium of small crystals—I. The case of the isotropic surface. Acta Metall. 28, 1333–1338 (1980).

  142. 142.

    et al. Reduced carbon solubility in Fe nano-clusters and implications for the growth of single-walled carbon nanotubes. Phys. Rev. Lett. 100, 195502 (2008).

  143. 143.

    et al. Density functionals for surface science: Exchange-correlation model development with Bayesian error estimation. Phys. Rev. B 85, 235149 (2012).

  144. 144.

    et al. Viscous state effect on the activity of Fe nano-catalysts. ACS Nano 4, 6950–6956 (2010).

  145. 145.

    , , & Beyond intercalation-based Li-ion batteries: The state of the art and challenges of electrode materials reacting through conversion reactions. Adv. Mater. 22, E170–E192 (2010).

  146. 146.

    et al. Prototype systems for rechargeable magnesium batteries. Nature 407, 724–727 (2000).

  147. 147.

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

  148. 148.

    & Ab initio calculations of cohesive energies of Fe-based glass-forming alloys. Phys. Rev. B 70, 144107 (2004).

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Acknowledgements

We thank Marco Fornari, Greg Rohrer, Shidong Wang, Kesong Yang, Junkai Xue, Richard Taylor, Camilo Calderon, Cheng-Ing Chia, Omar Knio, Ichiro Takeuchi, Mike Mehl, Harold Stokes, Rodney Forcade, Gerbrand Ceder, Alex Zunger, Wahyu Setyawan and Aleksey Kolmogorov for useful comments. This work was supported in part by DOD-ONR (N00014-11-1-0136, N00014-09-1-0921) and by the Duke University—Center for Materials Genomics. S.S. thanks financial support from CRANN.

Author information

Affiliations

  1. Department of Mechanical Engineering and Materials Science, and Department of Physics, Duke University, Durham, North Carolina 27708, USA

    • Stefano Curtarolo
    •  & Ohad Levy
  2. Center for Materials Genomics, Duke University, Durham, North Carolina 27708, USA

    • Stefano Curtarolo
    • , Gus L. W. Hart
    • , Marco Buongiorno Nardelli
    • , Natalio Mingo
    • , Stefano Sanvito
    •  & Ohad Levy
  3. Department of Physics and Astronomy, Brigham Young University, Provo, Utah 84602, USA

    • Gus L. W. Hart
  4. Department of Physics and Department of Chemistry, University of North Texas, Denton, Texas 76203

    • Marco Buongiorno Nardelli
  5. Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA

    • Marco Buongiorno Nardelli
  6. LITEN, CEA-Grenoble, 17 rue des Martyrs, 38054 Grenoble Cedex 9, France

    • Natalio Mingo
  7. School of Physics and CRANN, Trinity College, Dublin 2, Ireland

    • Stefano Sanvito
  8. Department of Physics, NRCN, PO Box 9001, Beer-Sheva 84190, Israel

    • Ohad Levy

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Correspondence to Stefano Curtarolo.

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