Skip to main content

News about Scitation

In December 2016 Scitation will launch with a new design, enhanced navigation and a much improved user experience.

To ensure a smooth transition, from today, we are temporarily stopping new account registration and single article purchases. If you already have an account you can continue to use the site as normal.

For help or more information please visit our FAQs.

banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1.E. B. Tadmor, R. S. Elliott, S. R. Phillpot, and S. B. Sinnott, “NSF cyberinfrastructures: A new paradigm for advancing materials simulation,” Curr. Opin. Solid State Mater. Sci. 17, 298304 (2013).
2.B. G. Sumpter, R. K. Vasudevan, T. Potok, and S. V. Kalinin, “A bridge for accelerating materials by design,” npj Comput. Mater. 1, 15008 (2015).
3.G. B. Olson, “Designing a new material world,” Science 288, 993998 (2000).
4.C. L. Magee, “Towards quantification of the role of materials innovation in overall technological development,” Complexity 18, 1025 (2012).
5.J. A. Christodoulou, “Integrated computational materials engineering and materials genome initiative: Accelerating materials innovation,” Adv. Mater. Processes 171(3), 2831 (March 2013).
6.T. W. Eagar, “Bringing new materials to market,” Technol. Rev. 98(2), 4249 (1995).
7.C. O. Björling and A. Westgren, “Minerals of the Varuträsk pegmatite,” Geol. Foeren. Stockholm Foerh. 60, 6772 (1938).
8.A. Padhi, K. Nanjundaswamya, and J. Goodenough, “Phospho-olivines as positive-electrode materials for rechargeable lithium batteries,” J. Electrochem. Soc. 144, 11881194 (1997).
9.G. Ceder, G. Hautier, A. Jain, and S. P. Ong, “Recharging lithium battery research with first-principles methods,” MRS Bull. 36, 185191 (2011).
10.H. W. Meuer, E. Strohmaier, J. Dongarra, and H. D. Simon, The TOP500: History, Trends, and Future Directions in High Performance Computing (Chapman & Hall/CRC, 2014).
11.R. O. Jones, “Density functional theory: Its origins, rise to prominence, and future,” Rev. Mod. Phys. 87, 897 (2015).
12.A. D. Becke, “Perspective: Fifty years of density-functional theory in chemical physics,” J. Chem. Phys. 140, 18A301 (2014).
13.A. Jain, Y. Shin, and K. A. Persson, “Computational predictions of energy materials using density functional theory,” Nat. Rev. Mater. 1, 15004 (2016).
14.G. Hautier, A. Jain, and S. P. Ong, “From the computer to the laboratory: Materials discovery and design using first-principles calculations,” J. Mater. Sci. 47, 73177340 (2012).
15.R. Potyrailo, K. Rajan, K. Stoewe, I. Takeuchi, B. Chisholm, and H. Lam, “Combinatorial and high-throughput screening of materials libraries: Review of state of the art,” ACS Comb. Sci. 13, 579 (2011).
16.J.-C. Zhao, “High-throughput experimental tools for the materials genome initiative,” Chin. Sci. Bull. 59, 16521661 (2014).
17.J. Hattrick-Simpers, C. Wen, and J. Lauterbach, “The materials super highway: Integrating high-throughput experimentation into mapping the catalysis materials genome,” Catal. Lett. 145, 290298 (2014).
18.S. Nemšák, A. Shavorskiy, O. Karslioglu, I. Zegkinoglou, A. Rattanachata, C. S. Conlon, A. Keqi, P. K. Greene, E. C. Burks, F. Salmassi, E. M. Gullikson, S. Yang, K. Liu, H. Bluhm, and C. S. Fadley, “Concentration and chemical-state profiles at heterogeneous interfaces with sub-nm accuracy from standing-wave ambient-pressure photoemission,” Nat. Commun. 5, 5441 (2014).
19.D. S. Su, B. Zhang, and R. Schlögl, “Electron microscopy of solid catalysts—Transforming from a Challenge to a toolbox,” Chem. Rev. 115, 2818 (2015).
20.N. Balke, S. Jesse, A. N. Morozovska, E. Eliseev, D. W. Chung, Y. Kim, L. Adamczyk, R. E. García, N. Dudney, and S. V. Kalinin, “Nanoscale mapping of ion diffusion in a lithium-ion battery cathode,” Nat. Nanotechnol. 5, 749754 (2010).
21.M. E. Holtz, Y. Yu, D. Gunceler, J. Gao, R. Sundararaman, K. A. Schwarz, T. A. Arias, H. D. Abruña, and D. A. Muller, “Nanoscale imaging of lithium ion distribution during in situ operation of battery electrode and electrolyte,” Nano Lett. 14, 14531459 (2014).
22.P. Patel, “Materials genome initiative and energy,” MRS Bull. 36, 964966 (2011).
23.J. Allison, “Integrated computational materials engineering: A perspective on progress and future steps,” JOM 63, 1518 (2011).
24.National Science and Technology Council, Materials Genome Initiative Strategic Plan (National Science and Technology Council, 2014), available at
25.A. White, “The materials genome initiative: One year on,” MRS Bull. 37, 715716 (2012).
26.A. A. White, “Universities prepare next-generation workforce to benefit from the materials genome initiative,” MRS Bull. 38, 673674 (2013).
27.G. B. Olson and C. J. Kuehmann, “Materials genomics: From CALPHAD to flight,” Scr. Mater. 70, 2530 (2014).
28.A. A. White, “Interdisciplinary collaboration, robust funding cited as key to success of materials genome initiative program,” MRS Bull. 38, 894896 (2013).
29.A. White, “Workshop makes recommendations to increase diversity in materials science and engineering,” MRS Bull. 38, 120122 (2013).
30.L. Glasser, “Crystallographic information resources,” J. Chem. Educ. 93, 542 (2015).
31.Y. Le Page, “Data mining in and around crystal structure databases,” MRS Bull. 31, 991 (2006).
32.J. Faber and T. Fawcett, “The powder diffraction file: Present and future,” Acta Crystallogr., Sect. B: Struct. Sci. 58, 325332 (2002).
33.C. W. Bale, E. Bélisle, P. Chartrand, S. A. Decterov, G. Eriksson, K. Hack, I.-H. Jung, Y.-B. Kang, J. Melançon, A. D. Pelton, C. Robelin, and S. Petersen, “FactSage thermochemical software and databases—Recent developments,” Calphad 33, 295311 (2009).
34.P. J. Linstrom and W. G. Mallard, NIST Chemistry WebBook, NIST Standard Reference Database Number 69, 2013.
35.M. W. Chase and J. A. N. A. Force, NIST-JANAF thermochemical tables, 1998.
36.O. Kubaschewski, C. Alcock, and P. Spencer, Materials Thermochemistry, 6th ed. (Pergamom Press, Oxford, 1993).
37.T. B. Massalski and H. Okamoto, Binary Alloy Phase Diagrams, 2nd ed. (ASM International, 1990).
38.C. E. Campbell, U. R. Kattner, and Z. K. Liu, “File and data repositories for next generation CALPHAD,” Scr. Mater. 70, 711 (2014).
39.L. Kaufman and J. Ågren, “CALPHAD, first and second generation—Birth of the materials genome,” Scr. Mater. 70, 36 (2014).
40.A. A. White, “Mandates for public access to publications and data on the horizon for US researchers,” MRS Bull. 38, 531532 (2013).
41.J. R. Kitchin, “Data sharing in surface science,” Surf. Sci. (published online 2015).
42.J. R. Kitchin, “Examples of effective data sharing in scientific publishing,” ACS Catal. 5, 38943899 (2015).
43.D. Guevarra, A. Shinde, S. K. Suram, I. D. Sharp, F. Toma, J. A. Haber, and J. Gregoire, “Development of solar fuels photoanodes through combinatorial integration of Ni-La-Co-Ce oxide catalysts on BiVO4,” Energy Environ. Sci. 9, 565 (2015).
44.L. Cheng, R. S. Assary, X. Qu, A. Jain, S. P. Ong, N. N. Rajput, K. A. Persson, and L. A. Curtiss, “Accelerating electrolyte discovery for energy storage by high throughput screening,” J. Phys. Chem. Lett. 6, 283291 (2015).
45.X. Qu, A. Jain, N. N. Rajput, L. Cheng, Y. Zhang, S. P. Ong, M. Brafman, E. Maginn, L. A. Curtiss, and K. A. Persson, “The electrolyte genome project: A big data approach in battery materials discovery,” Comput. Mater. Sci. 103, 5667 (2015).
46.S. K. Suram, J. A. Haber, J. Jin, and J. M. Gregoire, “Generating information rich high-throughput experimental materials genomes using functional clustering via multi-tree genetic programming and information theory,” ACS Comb. Sci. 17, 224233 (2015).
47.L. Lin, “Materials databases infrastructure constructed by first principles calculations: A review,” Mater. Perform. Charact. 4, MPC20150014 (2015).
48.A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, and K. A. Persson, “Commentary: The materials project: A materials genome approach to accelerating materials innovation,” APL Mater. 1, 011002 (2013).
49.S. Curtarolo, W. Setyawan, S. Wang, J. Xue, K. Yang, R. H. Taylor, L. J. Nelson, G. L. W. Hart, S. Sanvito, M. Buongiorno-Nardelli, N. Mingo, and O. Levy, “AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations,” Comput. Mater. Sci. 58, 227235 (2012).
50.J. E. Saal, S. Kirklin, M. Aykol, B. Meredig, and C. Wolverton, “Materials design and discovery with high-throughput density functional theory: The open quantum materials database (OQMD),” JOM 65, 15011509 (2013).
51.M. De Jong, W. Chen, T. Angsten, A. Jain, R. Notestine, A. Gamst, M. Sluiter, C. K. Ande, S. Van Der Zwaag, J. J. Plata, C. Toher, S. Curtarolo, G. Ceder, K. A. Persson, and M. Asta, “Charting the complete elastic properties of inorganic crystalline compounds,” Sci. Data 2, 150009 (2015).
52.M. de Jong, W. Chen, H. Geerlings, M. Asta, and K. A. Persson, “A database to enable discovery and design of piezoelectric materials,” Sci. Data 2, 150053 (2015).
53.G. Yuan and F. Gygi, “ESTEST: A framework for the validation and verification of electronic structure codes,” Comput. Sci. Discovery 3, 015004 (2010).
54.See for the NoMaD repository.
55.A. Jain, G. Hautier, C. J. Moore, S. P. Ong, C. C. Fischer, T. Mueller, K. A. Persson, G. Ceder, S. Ping Ong, C. C. Fischer, T. Mueller, K. A. Persson, and G. Ceder, “A high-throughput infrastructure for density functional theory calculations,” Comput. Mater. Sci. 50, 22952310 (2011).
56.W. Setyawan and S. Curtarolo, “High-throughput electronic band structure calculations: Challenges and tools,” Comput. Mater. Sci. 49, 299312 (2010).
57.C. E. Calderon, J. J. Plata, C. Toher, C. Oses, O. Levy, M. Fornari, A. Natan, M. J. Mehl, G. Hart, M. B. Nardelli, and S. Curtarolo, “The AFLOW standard for high-throughput materials science calculations,” Comput. Mater. Sci. 108, 233238 (2015).
58.M. W. Penninger, C. H. Kim, L. T. Thompson, and W. F. Schneider, “DFT analysis of NO oxidation intermediates on undoped and doped LaCoO3 perovskite,” J. Phys. Chem. C 119, 2048820494 (2015).
59.Y. Zhu, X. He, and Y. Mo, “First principles study on electrochemical and chemical stability of the solid electrolyte-electrode interfaces in all-solid-state Li-ion batteries,” J. Mater. Chem. A 4, 3253 (2015).
60.Y. Zhu, X. He, and Y. Mo, “Origin of outstanding stability in the lithium solid electrolyte materials: Insights from thermodynamic analyses based on first principles calculations,” ACS Appl. Mater. Interfaces 7, 23658 (2015).
61.A. Narayan, A. Bhutani, S. Rubeck, J. N. Eckstein, D. P. Shoemaker, and L. K. Wagner, “Computational and experimental investigation of unreported transition metal selenides and sulphides,” e-print arXiv:1512.02214 [cond-mat.mtrl-sci] (2015).
62.M. Burbano, M. Duttine, O. Borkiewicz, A. Wattiaux, A. Demourgues, M. Salanne, H. Groult, and D. Dambournet, “Anionic ordering and thermal properties of FeF3 center dot 3H2O,” Inorg. Chem. 54, 96199625 (2015).
63.R. Sarmiento-Pérez, T. F. T. Cerqueira, S. Körbel, S. Botti, and M. A. L. Marques, “Prediction of stable nitride perovskites,” Chem. Mater. 27, 59575963 (2015).
64.I. Jandl, H. Ipser, and K. W. Richter, “Thermodynamic modelling of the general NiAs-type structure: A study of first principle energies of formation for binary Ni-containing B8 compounds,” Calphad 50, 174181 (2015).
65.E. B. Tadmor, R. S. Elliott, J. P. Sethna, R. E. Miller, and C. A. Becker, “The potential of atomistic simulations and the knowledge base of interatomic models,” J. Mater. 63, 17 (2011).
66.C. A. Becker, F. Tavazza, Z. T. Trautt, R. A. Buarque, and D. Macedo, “Considerations for choosing and using force fields and interatomic potentials in materials science and engineering,” Curr. Opin. Solid State Mater. Sci. 17, 277283 (2013).
67.G. P. Purja Pun, K. A. Darling, L. J. Kecskes, and Y. Mishin, “Angular-dependent interatomic potential for the Cu–Ta system and its application to structural stability of nano-crystalline alloys,” Acta Mater. 100, 377391 (2015).
68.M. A. Gibson and C. A. Schuh, “A survey of ab-initio calculations shows that segregation-induced grain boundary embrittlement is predicted by bond-breaking arguments,” Scr. Mater. 113, 5558 (2016).
69.J. Allison, D. Backman, and L. Christodoulou, “Integrated computational materials engineering: A new paradigm for the global materials profession,” JOM 58, 2527 (2006).
70.G. I. Miletic and A. Drašner, “DFT study of the cohesive and structural properties of Y Ni5Hx compounds,” J. Alloys Compd. 622, 10411048 (2015).
71.I. Valencia-Jaime, R. Sarmiento-Pérez, S. Botti, M. A. L. Marques, M. Amsler, S. Goedecker, and A. H. Romero, “Novel crystal structures for lithium–silicon alloy predicted by minima hopping method,” J. Alloys Compd. 655, 147154 (2016).
72.R. Sarmiento-Pérez, S. Botti, and M. A. L. Marques, “Optimized exchange and correlation semilocal functional for the calculation of energies of formation,” J. Chem. Theory Comput. 11, 38443850 (2015).
73.T. S. Jauho, T. Olsen, T. Bligaard, and K. S. Thygesen, “Improved description of metal oxide stability: Beyond the random phase approximation with renormalized kernels,” Phys. Rev. B 92, 115140 (2015).
74.V. S. Kandagal, M. D. Bharadwaj, and U. V. Waghmare, “Theoretical prediction of a highly conducting solid electrolyte for sodium batteries: Na10GeP2S12,” J. Mater. Chem. A 3, 12992 (2015).
75.G. M. Dongho Nguimdo and D. P. Joubert, “A density functional (PBE, PBEsol, HSE06) study of the structural, electronic and optical properties of the ternary compounds AgAlX2 (X = S, Se, Te),” Eur. Phys. J. B 88, 113 (2015).
76.M. C. Troparevsky, J. R. Morris, M. Daene, Y. Wang, A. R. Lupini, and G. M. Stocks, “Beyond atomic sizes and Hume-Rothery rules: Understanding and predicting high-entropy alloys,” JOM 67, 23502363 (2015).
77.S. P. Ong, W. D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V. L. Chevrier, K. A. Persson, and G. Ceder, “Python materials genomics (pymatgen): A robust, open-source python library for materials analysis,” Comput. Mater. Sci. 68, 314319 (2013).
78.I. E. Castelli, D. D. Landis, K. S. Thygesen, S. Dahl, I. Chorkendorff, T. F. Jaramillo, and K. W. Jacobsen, “New cubic perovskites for single-and two-photon water splitting using the computational materials repository,” Energy Environ. Sci. 5, 9034 (2012).
79.I. E. Castelli, T. Olsen, S. Datta, D. D. Landis, S. Dahl, K. S. Thygesen, and K. W. Jacobsen, “Computational screening of perovskite metal oxides for optimal solar light capture,” Energy Environ. Sci. 5, 5814 (2012).
80.T. Krishnamoorthy, H. Ding, C. Yan, W. L. Leong, T. Baikie, Z. Zhang, S. Li, M. Asta, N. Mathews, and S. G. Mhaisalkar, “Lead-free germanium iodide perovskite materials for photovoltaic application,” J. Mater. Chem. A 3, 2382923832 (2015).
81.Z. R. Liu and D. Y. Li, “Stability and formation of long period stacking order structure in Mg-based ternary alloys,” Comput. Mater. Sci. 103, 9096 (2015).
82.R. Sarmiento-Pérez, T. F. T. Cerqueira, I. Valencia-Jaime, M. Amsler, S. Goedecker, A. H. Romero, S. Botti, and M. A. L. Marques, “Novel phases of lithium-aluminum binaries from first-principles structural search,” J. Chem. Phys. 142, 024710 (2015).
83.Z.-H. Cai, P. Narang, H. A. Atwater, S. Chen, C.-G. Duan, Z.-Q. Zhu, and J.-H. Chu, “Cation-mutation design of quaternary nitride semiconductors lattice-matched to GaN,” Chem. Mater. 27, 7757 (2015).
84.K. Choudhary, T. Liang, K. Mathew, B. Revard, A. Chernatynskiy, S. R. Phillpot, R. G. Hennig, and S. B. Sinnott, “Dynamical properties of AlN nanostructures and heterogeneous interfaces predicted using COMB potentials,” Comput. Mater. Sci. 113, 8087 (2016).
85.M. Pandey, A. Vojvodic, K. S. Thygesen, and K. W. Jacobsen, “Two-dimensional metal dichalcogenides and oxides for hydrogen evolution: A computational screening approach,” J. Phys. Chem. Lett. 9, 15771585 (2015).
86.D. Sun, Q. Hu, J. Chen, X. Zhang, L. Wang, Q. Wu, and A. Zhou, “Structural transformation of MXene (V 2C, Cr2C, and Ta2C) with O groups during lithiation: A first principles investigation,” ACS Appl. Mater. Interfaces 8, 74 (2015).
87.F. Gschwind, G. Rodriguez-Garcia, D. J. S. Sandbeck, A. Gross, M. Weil, M. Fichtner, and N. Hörmann, “Fluoride ion batteries: Theoretical performance, safety, toxicity, and a combinatorial screening of new electrodes,” J. Fluorine Chem. 182, 7690 (2016).
88.W. T. Hong, R. E. Welsch, and Y. Shao-Horn, “Descriptors of oxygen-evolution activity for oxides: A statistical evaluation,” J. Phys. Chem. C 120, 7886 (2016).
89.A. Seko, A. Togo, H. Hayashi, K. Tsuda, L. Chaput, and I. Tanaka, “Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization,” Phys. Rev. Lett. 115, 205901 (2015).
90.T. Tada, S. Takemoto, S. Matsuishi, and H. Hosono, “High-throughput ab initio screening for two-dimensional electride materials,” Inorg. Chem. 53, 10347 (2014).
91.M. J. Young, M. Neuber, A. C. Cavanagh, H. Sun, C. B. Musgrave, and S. M. George, “Sodium charge storage in thin films of MnO2 derived by electrochemical oxidation of MnO atomic layer deposition films,” J. Electrochem. Soc. 162, A2753A2761 (2015).
92.J. C. Weber, P. T. Blanchard, A. W. Sanders, J. C. Gertsch, S. M. George, S. Berweger, A. Imtiaz, K. J. Coakley, T. M. Wallis, K. A. Bertness, P. Kabos, N. A. Sanford, and V. M. Bright, “GaN nanowire coated with atomic layer deposition of tungsten: A probe for near-field scanning microwave microscopy,” Nanotechnology 25, 415502 (2014).
93.T. T. Tran and M. N. Obrovac, “Alloy negative electrodes for high energy density metal-ion cells,” J. Electrochem. Soc. 158, A1411 (2011).
94.M. Yin, J. Hasier, and P. Nash, “A review of phase equilibria in Heusler alloy systems containing Fe, Co or Ni,” J. Mater. Sci. 51, 5070 (2015).
95.M. Yin, P. Nash, W. Chen, and S. Chen, “Standard enthalpies of formation of selected Ni2Y Z Heusler compounds,” J. Alloys Compd. 660, 258265 (2016).
96.M. Yin and P. Nash, “Intermetallics enthalpies of formation of selected Pd2Y Z Heusler compounds,” Intermetallics 58, 1519 (2015).
97.M. Yin and P. Nash, “Standard enthalpies of formation of selected Ru2Y Z Heusler compounds,” J. Alloys Compd. 634, 7074 (2015).
98.M. Yin and P. Nash, “Standard enthalpies of formation of selected XYZ half-Heusler compounds,” J. Chem. Thermodyn. 91, 17 (2015).
99.K. He, Y. Zhou, P. Gao, L. Wang, N. Pereira, G. G. Amatucci, K. W. Nam, X. Q. Yang, Y. Zhu, F. Wang, and D. Su, “Sodiation via heterogeneous disproportionation in FeF2 electrodes for sodium-ion batteries,” ACS Nano 8, 72517259 (2014).
100.A. J. Martinolich and J. R. Neilson, “Pyrite formation via kinetic intermediates through low-temperature solid-state metathesis,” J. Am. Chem. Soc. 136, 1565415659 (2014).
101.R. D. Bayliss, S. N. Cook, D. O. Scanlon, S. Fearn, J. Cabana, C. Greaves, J. A. Kilner, and S. J. Skinner, “Understanding the defect chemistry of alkali metal strontium silicate solid solutions: Insights from experiment and theory,” J. Mater. Chem. A 2, 1791917924 (2014).
102.B. Rousseau, V. Timoshevskii, N. Mousseau, M. Côté, and K. Zaghib, “A novel intercalation cathode material for sodium-based batteries,” Electrochem. Commun. 52, 912 (2015).
103.L. MacEachern, R. A. Dunlap, and M. N. Obrovac, “A combinatorial investigation of Fe-Si-Zn thin film negative electrodes for Li-ion batteries,” J. Electrochem. Soc. 162, A229A234 (2014).
104.T. Nagase and Y. Umakoshi, “Amorphous phase formation in Co–Cu–Zr–B-based immiscible alloys,” J. Alloys Compd. 649, 11741181 (2015).
105.Z. Du, T. D. Hatchard, R. A. Dunlap, and M. N. Obrovac, “Combinatorial investigations of Ni-Si negative electrode materials for Li-ion batteries,” J. Electrochem. Soc. 162, A1858A1863 (2015).
106.M. Fondell, T. J. Jacobsson, M. Boman, and T. Edvinsson, “Optical quantum confinement in low dimensional hematite,” J. Mater. Chem. A 2, 3352 (2014).
107.A. J. Cohen, P. Mori-Sánchez, and W. Yang, “Insights into current limitations of density functional theory,” Science 321, 792794 (2008).
108.S. P. Ong, S. Cholia, A. Jain, M. Brafman, D. Gunter, G. Ceder, and K. A. Persson, “The materials application programming interface (API): A simple, flexible and efficient API for materials data based on representational state transfer (REST) principles,” Comput. Mater. Sci. 97, 209215 (2015).
109.P. Huck, D. Gunter, S. Cholia, D. Winston, A. T. N’Diaye, and K. Persson, “User applications driven by the community contribution framework MPContribs in the materials project,” Concurr. Comput. Pract. Exp. (published online 2015).
110.P. Huck, A. Jain, D. Gunter, D. Winston, and K. Persson, “A community contribution framework for sharing materials data with materials project,” in IEEE 11th International Conference on e-Science (IEEE, 2015).
111.S. R. Hall, F. H. Allen, and I. D. Brown, “The crystallographic information file (CIF): A new standard archive file for crystallography,” Acta Crystallogr., Sect. A: Found. Crystallogr. 47, 655 (1991).
112.M. Folk, G. Heber, Q. Koziol, E. Pourmal, and D. Robinson, “An overview of the HDF5 technology suite and its applications,” in Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases (AD’11) (ACM, 2011), pp. 3647.
113.R. Rew and G. Davis, “NetCDF: An interface for scientific data access,” IEEE Comput. Graphics Appl. 10, 7682 (1990).
114.T. Bray, J. Paoli, C. M. Sperberg-McQueen, E. Maler, and F. Yergeau, “Extensible markup language (XML),” World Wide Web J. 2, 2766 (1997).
115.D. Crockford, “The application/json media type for javascript object notation (json),” RFC 7159, 2006.
116.P. Murray-Rust, H. S. Rzepa, and M. Wright, “Development of chemical markup language (CML) as a system for handling complex chemical content,” New J. Chem. 25, 618634 (2001).
117.J. G. Kaufman and E. F. Begley, “MatML: A data interchange markup language,” Adv. Mater. Process. 161, 3539 (2003).
118.See for MongoDB, I. MongoDB.
119.R. T. Fielding, Architectural Styles and the Design of Network-based Software Architectures (University of California, Irvine, 2000).
120.G. Kresse and J. Furthmüller, “Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,” Phys. Rev. B 54, 1116911186 (1996).
121.See Citrine Informatics Citrination materials search platform.
122.See for Citrine Informatics MIF Schema.
123.H. E. Pence and A. Williams, “ChemSpider: An online chemical information resource,” J. Chem. Educ. 87, 11231124 (2010).
124.J. A. Warren and R. F. Boisvert, “Building the materials innovation infrastructure: Data and standards,” NIST Report No. NISTIR 7898, 2012.
125.C. H. Ward, J. A. Warren, and R. J. Hanisch, “Making materials science and engineering data more valuable research products,” Integr. Mater. Manuf. Innovation 3, 22 (2014).
126.T. N. Bhat, L. M. Bartolo, U. R. Kattner, C. E. Campbell, and J. T. Elliott, “Strategy for extensible, evolving terminology for the materials genome initiative efforts,” JOM 67, 18661875 (2015).
127.M. W. Gaultois, T. D. Sparks, C. K. H. Borg, R. Seshadri, W. D. Bonificio, and D. R. Clarke, “Data-driven review of thermoelectric materials: Performance and resource considerations,” Chem. Mater. 25, 2911 (2013).
128.P. Gorai, D. Gao, B. Ortiz, S. Miller, S. A. Barnett, T. Mason, Q. Lv, V. Stevanović, and E. S. Toberer, “TE design lab: A virtual laboratory for thermoelectric material design,” Comput. Mater. Sci. 112, 368376 (2016).
129.G. Hautier, S. S. P. Ong, A. Jain, C. C. J. Moore, and G. Ceder, “Accuracy of density functional theory in predicting reaction energies from binary to ternary oxides and its implication on phase stability,” Phys. Rev. B 75, 155208 (2011).
130.A. Jain, G. Hautier, S. P. Ong, C. J. Moore, C. C. Fischer, K. A. Persson, and G. Ceder, “Formation enthalpies by mixing GGA and GGA + U calculations,” Phys. Rev. B 84, 045115 (2011).
131.S. Grindy, B. Meredig, S. Kirklin, J. E. Saal, and C. Wolverton, “Approaching chemical accuracy with density functional calculations: Diatomic energy corrections,” Phys. Rev. B 87, 075150 (2013).
132.V. Stevanovi, X. Zhang, and A. Zunger, “Correcting density functional theory for accurate predictions of compound enthalpies of formation: Fitted elemental-phase reference energies (FERE),” Phys. Rev. B 85, 115104 (2011).
133.S. Lany, “Semiconductor thermochemistry in density functional calculations,” Phys. Rev. B 78, 18 (2008).
134.R. van Noorden, “Interdisciplinary research by the numbers,” Nature 525, 306 (2015).
135.A. A. White, “Big data are shaping the future of materials science,” MRS Bull. 38, 594595 (2013).
136.S. R. Kalidindi and M. De Graef, “Materials data science: Current status and future outlook,” Annu. Rev. Mater. Res. 45, 171193 (2015).
137.V. Marx, “The big challenges of big data,” Nature 498, 255260 (2013).
138.M. Wilde, M. Hategan, J. M. Wozniak, B. Clifford, D. S. Katz, and I. Foster, “Swift: A language for distributed parallel scripting,” Parallel Comput. 37, 633652 (2011).
139.G. Pizzi, A. Cepellotti, R. Sabatini, N. Marzari, and B. Kozinsky, “AiiDA: Automated interactive infrastructure and database for computational science,” Comput. Mater. Sci. 111, 218 (2016).
140.A. Jain, S. P. Ong, W. Chen, B. Medasani, X. Qu, M. Kocher, M. Brafman, G. Petretto, G.-M. Rignanese, G. Hautier, D. Gunter, and K. A. Persson, “FireWorks: A dynamic workflow system designed for high-throughput applications,” Concurr. Comput. Pract. Exp. 27, 5037 (2015).
141.Y. Bengio, “Learning deep architectures for AI,” Found. Trends Mach. Learn. 2, 1 (2009).
142.L. Deng and D. Yu, “Deep learning: Methods and applications,” Found. Trends Signal Process. 7, 197387 (2013).
143.B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum, “Human-level concept learning through probabilistic program induction,” Science 350, 13321338 (2015).
144.K. Rajan, “Materials informatics,” Mater. Today 8, 3548 (2005).
145.G. Mulholland and B. Meredig, “Hackathon aims to solve materials problems,” MRS Bull. 40, 166167 (2015).
146.A. White, “Federal agencies announce materials data challenge,” MRS Bull. 40, 906907 (2015).

Data & Media loading...


Article metrics loading...



Materials innovations enable new technological capabilities and drive major societal advancements but have historically required long and costly development cycles. The Materials Genome Initiative (MGI) aims to greatly reduce this time and cost. In this paper, we focus on data reuse in the MGI and, in particular, discuss the impact of three different computational databases based on density functional theory methods to the research community. We also discuss and provide recommendations on technical aspects of data reuse, outline remaining fundamental challenges, and present an outlook on the future of MGI’s vision of data sharing.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd