Computational predictions of energy materials using density functional theory

Nature Reviews Materials - Tập 1 Số 1
Anubhav Jain1, Yongwoo Shin1, Kristin A. Persson1
1Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, 94720, California, USA

Tóm tắt

Từ khóa


Tài liệu tham khảo

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Curtarolo, S., Morgan, D., Persson, K., Rodgers, J. & Ceder, G. Predicting crystal structures with data mining of quantum calculations. Phys. Rev. Lett. 91, 135503 (2003).

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

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

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

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

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

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

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

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

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

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

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

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

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