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.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Molecular design with automated quantum computing-based deep learning and optimization
npj Computational Materials Open Access 14 August 2023
-
Vacancy-mediated anomalous phononic and electronic transport in defective half-Heusler ZrNiBi
Nature Communications Open Access 05 August 2023
-
A robotic platform for the synthesis of colloidal nanocrystals
Nature Synthesis Open Access 02 March 2023
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout





References
Descartes, R. Meditations on First Philosophy: with Selections from the Objections and Replies (ed. Cottingham, J. ) (Cambridge Univ. Press, 1996).
Franceschetti, A. & Zunger, A. The inverse band structure problem: find the atomic configuration with given electronic properties. Nature 402, 60–63 (1999).
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).
Eperon, G. et al. Perovskite-perovskite tandem photovoltaics with optimized band gaps. Science 20, aaf9717 (2016).
Buzea, C. & Yamashita, T. Review of the superconducting properties of MgB2. Superconductor Sci. Technol. 14, R115–R146 (2001).
Luo, H. et al. Optical and structural properties of single phase epitaxial p-type transparent oxide thin films. Adv. Mater. 21, 3604–3607 (2007).
Center for Material Genomics, Material Science, Duke University et al. AFLOW database of calculated materials properties. AFLOWhttp://aflowlib.org/ (2016).
Materials Project. The Materials Project Database for calculated properties. Materials Projecthttps://www.materialsproject.org/ (2012).
Wolverton, C. et al. The Open Quantum Materials database for calculated properties. OQMDhttp://oqmd.org/ (2017).
Villars, P. et al. The Pauling File database of measured materials properties. Pauling Filehttp://paulingfile.com/index.php?p¼home (2016).
FIZ Karlsruhe. Inorganic Crystal Structure Database (ICSD). FIZ Karlsruhe ICSDhttp://www2.fiz-karlsruhe.de/icsd_home.html (2017).
Yang, D. et al. Functionality-directed First-principles screening of hybrid organic-inorganic perovskites with target intrinsic photovoltaic functionalities. Chem. Mater. 29, 524 (2017).
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).
Kim, J. C. et al. Synthesis and electrochemical properties of monoclinic LiMnBO3 as a Li intercalation material. J. Electrochem. Soc. 158, A309–A315 (2011).
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).
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).
Yan, J. et al. Materials descriptors for predicting thermoelectric performance. Energy Environ. Sci. 8, 983–994 (2015).
Jain, A., Shin, Y. & Persson, K. A. Computational predictions of energy materials using density functional theory. Nat. Rev. Mater. 1, 15004 (2016).
Gautier, R. et al. Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds. Nat. Chem. 7, 308–316 (2015).
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).
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).
Yu, L. & Zunger, A. Identification of potential photovoltaic absorbers based on first-principles spectroscopic screening of materials. Phys. Rev. Lett. 108, 068701 (2012).
Eigler, D. M. & Schweizer, E. K. Positioning single atoms with a scanning tunnelling microscope. Nature 344, 524–526 (1990).
Nadj-Perge, S. et al.Observation of Majorana fermions in ferromagnetic atomic chains on a superconductor. Science 346, 602–607 (2014).
Sitter, H. & Herman, M. A. Molecular Beam Epitaxy Fundamentals and Current Status (Springer-Verlag, Berlin, Heidelberg, 2012).
Mundy, J. A. et al Atomically engineered ferroic layers yield a roomtemperature magnetoelectric multiferroic. Nature 537, 523 (2016).
Caroff, P., et al. Controlled polytypic and twin-plane superlattices in III–V nanowires. Nat. Nanotech. 4, 50. (2009).
Algra, R. E., et al., Twinning superlattices in indium phosphide nanowires. Nature 456, 369 (2008).
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).
Yoshioka, D. The Quantum Hall Effect (Springer-Verlag, Berlin, Heidelberg, 2002).
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).
Hwang, H. Y. et al. Emergent phenomena at oxide interfaces. Nat. Mater. 11, 103–113 (2012).
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).
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).
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).
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).
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).
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).
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).
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).
Deng, J., Zunger, A. & Liu, J. Z. Cation ordering induced polarization enhancement for PbTiO3-SrTiO3 ferroelectric-dielectric superlattices. Phys. Rev. B 91, 081302 (2015)
Lewerenz, H.-J. & Peter, L. Photoelectrochemical Water Splitting: Materials, Processes and Architectures (RSC Publishing, 2013).
Luque, A., Hegedus, S., Delahoy, A. E. & Guo, S. Transparent Conducting Oxides for Photovoltaics (John Wiley, 2011).
Sarmadian, N. et al. High throughput first principles calculations of bixbyite oxides for TCO applications. Phys. Chem. Chem. Phys. 16, 17724–17733 (2014).
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).
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).
Zhao, L.-D. et al. Ultrahigh power factor and thermoelectric performance in hole-doped single-crystal SnSe. Science 351, 141–144 (2016).
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).
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).
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).
Zhang, X., Zhang, L., Perkins, J. D. & Zunger, A. Intrinsic transparent conductors without doping. Phys. Rev. Lett. 115, 176602 (2015).
Walsh, A. & Zunger, A. Instilling defect tolerance in new compounds. Nat. Mater. 16, 964 (2017).
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).
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).
Anderson, P. W. Concepts in Solids (Frontiers in Physics) (Benjamin, 1963).
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).
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).
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).
Luttinger, J. M. & Kohn, W. Motion of electrons and holes in perturbed periodic fields. Phys. Rev. 97, 869 (1955).
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).
Konig, M. et al. Quantum spin hall insulator state in HgTe quantum wells. Science 318, 766–770 (2007).
Ediger, M. et al. Peculiar many-body effects revealed in the spectroscopy of heavily charged quantum dots. Nat. Phys. 3, 774–779 (2007).
Mlinar, V. & Zunger, A. Spectral barcoding of quantum dots: Deciphering structural motifs from the excitonic spectra. Phys. Rev. B 80, 035328 (2009).
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).
Akopian, N. et al Entangled photon pairs from semiconductor quantum dots. Phy. Rev. Lett. 96, 13050 (2006).
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).
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).
Hart, G., Blum, V., Walorski, M. & Zunger, A. Genetic determination of first-principles Hamiltonians. Nat. Mater. 4, 391 (2005).
Rabe, K. M. & Waghmare, U. V. First-principles model Hamiltonians for ferroelectric transitions. Ferroelectrics 151, 69 (1994).
Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiment. Nature 533, 73–76 (2016).
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).
Hansen, K. et al. Assessment and validation of machine learning methods for predicting molecular atomization energies. J. Chem. Theory Comput. 9, 3404–3419 (2013).
Pilania, G. et al. Machine learning bandgaps of double perovskites. Sci. Rep. 6, 19375 (2016).
Sanchez, J. M., Ducastelle, F. & Gratias, D. Generalized cluster description of multicomponent systems. Physica A 128, 334–350 (1984).
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).
Wang, S. et al. Assessing the thermoelectric properties of sintered compounds via high throughput ab initio calculations. Phys. Rev. X 1, 021012 (2001).
Mooser, E. & Pearson, W. B. On the crystal chemistry of normal valence compounds. Acta Crystallograph. 12, 1015 (1959).
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).
Pettifor, D. Phenomenological and microscopic theories of structural stability. J. Less Common Metals 114, 7 (1985).
Sabatier, P. C. Past and future of inverse problems. J. Math. Phys. 41, 4082 (2000).
Wood, D. M. & Zunger, A. Epitaxial effects on coherent phase diagrams of alloys. Phys. Rev. B 40, 4062 (1989).
Zunger, A. in Handbook of Crystal Growth (ed. Hurle, D. T. J. ) 998–1047 (Elsevier, 1994).
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).
Glover, F. & Hanafi, S. Metaheuristic search with inequalities and target objectives for mixed binary optimization. Int. J. Appl. Metaheurist. Comput. 1, 1–17 (2010).
Bendsøe, M. P. & Kikuchi, N. Generating optimal topologies in structural design using a homogenization method. Computer Methods Appl. Mechan. Engineer. 71, 197–224 (1988).
Wang, M., Hu, X., Beratan, D. N. & Yang, W. Designing molecules by optimizing potentials. J. Am. Chem. Soc. 128, 3228 (2006).
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).
De Gironcoli, S., Giannozzi, P. & Baroni, S. Phonon dispersions in GaxAl1–x As alloys. Phys. Rev. Lett. 66, 2116 (1990).
Liu, Q. & Zunger, A. Predicted realization of cubic dirac fermion in quasi-one-dimensional transition-metal mono chalcogenides. Phys. Rev. X 7, 021019 (2017).
Sokolov, A. N. et al. From computational discovery to experimental characterization of a high hole mobility organic crystal. Nat. Commun. 2, 437 (2011).
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).
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).
Curtarolo, S. et al. The high-throughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).
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).
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).
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).
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).
Dyer, M. S. et al. Computationally assisted identification of functional inorganic materials. Science 340, 847–852 (2013).
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).
Young, S. M. et al. Dirac semimetal in three dimensions Phys. Rev. Lett. 108, 140405 (2012).
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).
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).
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).
Sun, W. et al. The thermodynamic scale of inorganic crystalline metastability. Sci. Adv. 2, 11 (2016).
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).
Trimarchi, G. & Zunger, A. Global space group optimization problem: Finding the stablest crystal structure without constraints. Phys. Rev. B 75, 104113 (2007).
Revard, B. C., Tipton, W. W. & Hennig, R. G. Structure and stability prediction of compounds with evolutionary algorithms. Top. Curr. Chem. 345, 181 (2014).
Pickard, C. J. & Needs, R. J. Ab initio random structure searching. J. Phys. Condens. Matter 23, 053201 (2011).
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).
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The author declares no competing interest.
Rights and permissions
About this article
Cite this article
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
Published:
DOI: https://doi.org/10.1038/s41570-018-0121
This article is cited by
-
A robotic platform for the synthesis of colloidal nanocrystals
Nature Synthesis (2023)
-
Vacancy-mediated anomalous phononic and electronic transport in defective half-Heusler ZrNiBi
Nature Communications (2023)
-
Inverse Hamiltonian design by automatic differentiation
Communications Physics (2023)
-
Molecular design with automated quantum computing-based deep learning and optimization
npj Computational Materials (2023)
-
Advances in machine learning- and artificial intelligence-assisted material design of steels
International Journal of Minerals, Metallurgy and Materials (2023)