Abstract
The Materials Project (MP) is a community resource for theory-based data, web-based materials analysis tools, and software for performing and analyzing calculations. The MP database includes a variety of computed properties such as crystal structure, energy, electronic band structure, and elastic tensors for tens of thousands of inorganic compounds. At the time of writing, over 40,000 users have registered for the MP database. These users interact with this data either through the MP web site (https://www.materialsproject.org) or through a REpresentational State Transfer (REST) application programming interface (API). MP also develops or contributes to several open-source software libraries to help set up, automate, analyze, and extract insight from calculation results. Furthermore, MP is developing tools to help researchers share their data (both computational and experimental) through its platform. The ultimate goal of these efforts is to accelerate materials design and education by providing new data and software tools to the research community. In this chapter, we review the history, theoretical methods, impact (including user-led research studies), and future goals for the Materials Project.
This is a preview of subscription content, access via your institution.
References
Ashton M, Paul J, Sinnott SB, Hennig RG (2017) Topology-scaling identification of layered solids and stable exfoliated 2D materials. Phys Rev Lett 118:106101
Barber CB, Dobkin DP, Huhdanpaa H (1996) The quickhull algorithm for convex hulls. ACM Trans Math Softw 22(4):469–483. http://doi.acm.org/10.1145/235815.235821
Bayliss RD, Cook SN, Scanlon DO, Fearn S, Cabana J, Greaves C, Kilner JA, Skinner SJ (2014) Understanding the defect chemistry of alkali metal strontium silicate solid solutions: insights from experiment and theory. J Mater Chem A 2:17919–17924
Belsky A, Hellenbrandt M, Karen VL, Luksch P (2002) New developments in the inorganic crystal structure database (ICSD): accessibility in support of materials research and design. Acta Crystall Sect B Struct Sci 58(3):364–369
Bray T (2017) The javascript object notation (JSON) data interchange format. STD 90, RFC 8259. https://www.rfc-editor.org/info/rfc8259
Cattell R (2011) Scalable SQL and NOSQL data stores. SIGMOD Rec 39(4):12–27. http://doi.acm.org/10.1145/1978915.1978919
Chen W, Pohls JH, Hautier G, Broberg D, Bajaj S, Aydemir U, Gibbs ZM, Zhu H, Asta M, Snyder GJ, Meredig B, White MA, Persson K, Jain A (2016) Understanding thermoelectric properties from high-throughput calculations: trends, insights, and comparisons with experiment. J Mater Chem C 4:4414–4426
Cheon G, Duerloo KAN, Sendek AD, Porter C, Chen Y, Reed EJ (2017) Data mining for new two- and one-dimensional weakly bonded solids and lattice-commensurate heterostructures. Nano Lett 17:1915–1923
Choudhary K, Kalish I, Beams R, Tavazza F (2017) High-throughput identification and characterization of two-dimensional materials using density functional theory. Sci Rep 7:5179
Cococcioni M, de Gironcoli S (2005) Linear response approach to the calculation of the effective interaction parameters in the LDA + U method. Phys Rev B 71:035105. https://link.aps.org/doi/10.1103/PhysRevB.71.035105
Dagdelen J, Montoya J, de Jong M, Persson K (2017) Computational prediction of new auxetic materials. Nat Commun 8:323
de Jong M, Chen W, Angsten T, Jain A, Notestine R, Gamst A, Sluiter M, Krishna Ande C, van der Zwaag S, Plata JJ, Toher C, Curtarolo S, Ceder G, Persson KA, Asta M (2015a) Charting the complete elastic properties of inorganic crystalline compounds. Sci Data 2:150009. https://doi.org/10.1038/sdata.2015.9, http://www.nature.com/articles/sdata20159
de Jong M, Chen W, Geerlings H, Asta M, Persson KA (2015b) A database to enable discovery and design of piezoelectric materials. Sci Data 2:150053. https://doi.org/10.1038/sdata.2015.53, http://www.nature.com/articles/sdata201553
de Jong M, Chen W, Notestine R, Persson K, Ceder G, Jain A, Asta M, Gamst A (2016) A statistical learning framework for materials science: application to elastic moduli of k-nary inorganic polycrystalline compounds. Sci Rep 6:34256. https://doi.org/10.1038/srep34256, http://www.ncbi.nlm.nih.gov/pubmed/27694824, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5046120
Dja (2015) Django (version 1.8): the web framework for perfectionists with deadlines. https://djangoproject.com
Dozier A, Persson K, Ong SP, Mathew K, Zheng C, Chen C, Kas J, Vila F, Rehr J (2017) Creation of an xas and eels spectroscopy resource within the materials project using feff9. Microscopy Microanalysis 23(S1):208–209
Elliot J, Vowell L, Nelson J, Ensor N, Robinson C, Studwell S, Martin M (2016) U.S. Department of Energy Office of Scientific and Technical Information (OSTI). https://www.osti.gov
Faber F, Lindmaa A, von Lilienfeld OA, Armiento R (2015) Crystal structure representations for machine learning models of formation energies. Int J Quant Chem 115(16):1094–1101. http://doi.wiley.com/10.1002/qua.24917
Gonze X, Jollet F, Araujo FA, Adams D, Amadon B, Applencourt T, Audouze C, Beuken JM, Bieder J, Bokhanchuk A, Bousquet E, Bruneval F, Caliste D, Côté M, Dahm F, Pieve FD, Delaveau M, Gennaro MD, Dorado B, Espejo C, Geneste G, Genovese L, Gerossier A, Giantomassi M, Gillet Y, Hamann D, He L, Jomard G, Janssen JL, Roux SL, Levitt A, Lherbier A, Liu F, Lukacevic I, Martin A, Martins C, Oliveira M, Poncé S, Pouillon Y, Rangel T, Rignanese GM, Romero A, Rousseau B, Rubel O, Shukri A, Stankovski M, Torrent M, Setten MV, troeye BV, Verstraete M, Waroquier D, Wiktor J, Xue B, Zhou A, Zwanziger J (2016) Recent developments in the ABINIT software package. Comput Phys Commun 205:106. https://doi.org/10.1016/j.cpc.2016.04.003, http://www.sciencedirect.com/science/article/pii/S0010465516300923
Grindy S, Meredig B, Kirklin S, Saal JE, Wolverton C (2013) Approaching chemical accuracy with density functional calculations: diatomic energy corrections. Phys Rev B 87(7):075150
Gunter D, Cholia S, Jain A, Kocher M, Persson K, Ramakrishnan L, Ong SP, Ceder G (2012) Community accessible datastore of high-throughput calculations: experiences from the materials project. In: 2012 SC companion: high performance computing, networking storage and analysis, pp 1244–1251. https://doi.org/10.1109/SC.Companion.2012.150
Hart GL, Forcade RW (2008) Algorithm for generating derivative structures. Phys Rev B 77(22):224115
Hautier G, Jain A, Ong SP (2012) From the computer to the laboratory: materials discovery and design using first-principles calculations. J Mater Sci 47:7317–7340
Huck P (2016a) Continuous and high-throughput allocation of digital object identifiers for computed and contributed materials data in the materials project – invited talk at reproducibility mini-symposium of SciPy16. https://youtu.be/bHhuO4EOgEw
Huck P (2016b) MPCite GitHub Repository. https://github.com/materialsproject/MPCite
Huck P (2016c) MPContribs GitHub Repository. https://github.com/materialsproject/MPContribs
Huck P (2017) Materials project: a prime case of software engineering in materials sciences. https://youtu.be/rs8b8HaA3_I
Huck P, Gunter D, Cholia S, Winston D, N’Diaye A, Persson KA (2015a) User applications driven by the community contribution framework MPContribs in the materials project. http://arxiv.org/abs/1510.05727
Huck P, Jain A, Gunter D, Winston D, Persson KA (2015b) A community contribution framework for sharing materials data with materials project. http://arxiv.org/abs/1510.05024
Huck P, Gunter D, Persson K, Cholia S, Morgan D, Wu H, Mayeshiba T (2016a) Effective and interactive dissemination of diffusion data using MPContribs. http://sciencegateways.org/wp-content/uploads/2016/09/Patrick-Huck-2016-11-02_Gateways2016-1.pdf
Huck P, Jain A, Gunter D, Cholia S, Winston D, Persson K (2016b) Materials project as analysis and validation hub for experimental and computational materials data. http://www.mrs.org/technical-programs/programs_abstracts/2016_mrs_fall_meeting_exhibit/tc2/tc2_5_3/tc2_5_06_6
Jain A, Hautier G, Moore CJ, Ong SP, Fischer CC, Mueller T, Persson KA, Ceder G (2011a) A high-throughput infrastructure for density functional theory calculations. Comput Mater Sci 50(8):2295–2310
Jain A, Hautier G, Ong SP, Moore CJ, Fischer CC, Persson KA, Ceder G (2011b) Formation enthalpies by mixing gga and gga + u calculations. Phys Rev B 84:045115. https://link.aps.org/doi/10.1103/PhysRevB.84.045115
Jain A, Ong SP, Chen W, Medasani B, Qu X, Kocher M, Brafman M, Petretto G, Rignanese GM, Hautier G, Gunter D, Persson KA (2015) Fireworks: a dynamic workflow system designed for high-throughput applications. Concurr Comput Pract Exp 27(17):5037–5059. https://doi.org/10.1002/cpe.3505, cPE-14-0307.R2
Jain A, Hautier G, Ong SP, Persson K (2016a) New opportunities for materials informatics: resources and data mining techniques for uncovering hidden relationships. J Mater Res 31(08):977–994. https://doi.org/10.1557/jmr.2016.80, http://www.journals.cambridge.org/abstract_S0884291416000807
Jain A, Persson KA, Ceder G (2016b) Research update: the materials genome initiative: data sharing and the impact of collaborative ab initio databases. APL Mater 4(5):053102. http://aip.scitation.org/doi/abs/10.1063/1.4944683
Jain A, Shin Y, Persson KA (2016c) Computational predictions of energy materials using density functional theory. Nat Rev Mater 1:15004
Kohn W, Sham LJ (1965) Self-consistent equations including exchange and correlation effects. Phys Rev 140:A1133–A1138. https://link.aps.org/doi/10.1103/PhysRev.140.A1133
Kong J, White CA, Krylov AI, Sherrill D, Adamson RD, Furlani TR, Lee MS, Lee AM, Gwaltney SR, Adams TR et al (2000) Q-chem 2.0: a high-performance ab initio electronic structure program package. J Comput Chem 21(16):1532–1548
Kresse G, Furthmüller J (1996) Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput Mater Sci 6(1):15–50. https://doi.org/10.1016/0927-0256(96)00008-0, http://www.sciencedirect.com/science/article/pii/0927025696000080
Kresse G, Hafner J (1994) Norm-conserving and ultrasoft pseudopotentials for first-row and transition elements. J Phys Condens Matter 6(40):8245–8257. http://iopscience.iop.org/article/10.1088/0953-8984/6/40/015
Krishnamoorthy T, Ding H, Yan C, Leong WL, Baikie T, Zhang Z, Sherburne M, Li S, Asta M, Mathews N, Mhaisalkar SG (2015) Lead-free germanium iodide perovskite materials for photovoltaic applications. J Mater Chem A 3:23829–23832
Lau CY, Dunstan MT, Hu W, Grey CP, Scott SA (2017) Large scale in silico screening of materials for carbon capture through chemical looping. Ener Env Sci 10:818–831
Lejaeghere K, Bihlmayer G, Björkman T, Blaha P, Blügel S, Blum V, Caliste D, Castelli IE, Clark SJ, Dal Corso A, de Gironcoli S, Deutsch T, Dewhurst JK, Di Marco I, Draxl C, Dułak M, Eriksson O, Flores-Livas JA, Garrity KF, Genovese L, Giannozzi P, Giantomassi M, Goedecker S, Gonze X, Grånäs O, Gross EKU, Gulans A, Gygi F, Hamann DR, Hasnip PJ, Holzwarth NAW, Iuşan D, Jochym DB, Jollet F, Jones D, Kresse G, Koepernik K, Küçükbenli E, Kvashnin YO, Locht ILM, Lubeck S, Marsman M, Marzari N, Nitzsche U, Nordström L, Ozaki T, Paulatto L, Pickard CJ, Poelmans W, Probert MIJ, Refson K, Richter M, Rignanese GM, Saha S, Scheffler M, Schlipf M, Schwarz K, Sharma S, Tavazza F, Thunström P, Tkatchenko A, Torrent M, Vanderbilt D, van Setten MJ, Van Speybroeck V, Wills JM, Yates JR, Zhang GX, Cottenier S (2016) Reproducibility in density functional theory calculations of solids. Science 351(6280). https://doi.org/10.1126/science.aad3000, http://science.sciencemag.org/content/351/6280/aad3000
Martinolich AJ, Neilson JR (2014) Pyrite formation via kinetic intermediates through low-temperature solid-state metathesis. J Am Chem Soc 136:15654–15659
Mathew K, Ong SP, Winston D, Montoya J, Aykol M, Dwaraknath S, Huck P (2016) Assets for the 2016 materials project workshop. https://doi.org/10.5281/zenodo.1040432
Mathew K, Montoya JH, Faghaninia A, Dwarakanath S, Aykol M, Tang H, Heng Chu I, Smidt T, Bocklund B, Horton M, Dagdelen J, Wood B, Liu ZK, Neaton J, Ong SP, Persson K, Jain A (2017) Atomate: a high-level interface to generate, execute, and analyze computational materials science workflows. Comput Mater Sci 139(Supplement C):140–152. https://doi.org/10.1016/j.commatsci.2017.07.030, http://www.sciencedirect.com/science/article/pii/S0927025617303919
Ong (2015) 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 97:209–215. https://doi.org/10.1016/j.commatsci.2014.10.037, http://www.sciencedirect.com/science/article/pii/S0927025614007113
Ong SP, Wang L, Kang B, Ceder G (2008) Li- fe- p- o2 phase diagram from first principles calculations. Chem Mater 20(5):1798–1807
Ong SP, Richards WD, Jain A, Hautier G, Kocher M, Cholia S, Gunter D, Chevrier VL, Persson KA, Ceder G (2013) Python materials genomics (pymatgen): a robust, open-source python library for materials analysis. Comput Mater Sci 68:314–319. https://doi.org/10.1016/j.commatsci.2012.10.028, http://www.sciencedirect.com/science/article/pii/S0927025612006295
Ong SP, Qu X, Richards W, Dacek S, Jain A, Hautier G, Kitchaev D (2014) Custodian: a simple, robust and flexible just-in-time job management framework in python. https://doi.org/10.5281/zenodo.11714
Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77:3865–3868. https://link.aps.org/doi/10.1103/PhysRevLett.77.3865
Perdew JP, Ernzerhof M, Zupan A, Burke K (1998) Nonlocality of the density functional for exchange and correlation: physical origins and chemical consequences. J Chem Phys 108(4):1522–1531
Persson KA, Waldwick B, Lazic P, Ceder G (2012) Prediction of solid-aqueous equilibria: scheme to combine first-principles calculations of solids with experimental aqueous states. Phys Rev B 85:235438. https://link.aps.org/doi/10.1103/PhysRevB.85.235438
Petousis I, Mrdjenovich D, Ballouz E, Liu M, Winston D, Chen W, Graf T, Schladt TD, Persson KA, Prinz FB (2017) High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials. Sci Data 4. https://www.nature.com/articles/sdata2016134
Ragan-Kelley M, Perez F, Granger B, Kluyver T, Ivanov P, Frederic J, Bussonnier M (2014) The jupyter/ipython architecture: a unified view of computational research, from interactive exploration to communication and publication. In: AGU fall meeting abstracts
Raicu I, Foster IT, Zhao Y (2008) Many-task computing for grids and supercomputers. In: 2008 workshop on many-task computing on grids and supercomputers, pp 1–11. https://doi.org/10.1109/MTAGS.2008.4777912
Ricci F, Chen W, Aydemir U, Snyder GJ, Rignanese GM, Jain A, Hautier G (2017) Data descriptor: an ab initio electronic transport database for inorganic materials. Sci Data 4:170085
Sendek AD, Yang Q, Cubuk ED, Duerloo KAN, Cui Y, Reed EJ (2017) Holistic computational structure screening of more than 12,000 candidates for solid lithium-ion conductor materials. Ener Env Sci 10:306–320
Shandiz MA, Gauvin R (2016) Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries. Comput Mater Sci 117:270–278
Shi J, Cerqueira TFT, Cui W, Nogueira F, Botti S, Marques MAL (2017) High-throughput search of ternary chalcogenides for p-type transparent electrodes. Sci Rep 7:43179
Singh AK, Zhou L, Shinde A, Suram SK, Montoya JH, Winston D, Gregoire JM, Persson KA (2017) Electrochemical stability of metastable materials. Chemistry of Materials p acs.chemmater.7b03980, http://pubs.acs.org/doi/abs/10.1021/acs.chemmater.7b03980
Sun W, Dacek ST, Ong SP, Hautier G, Jain A, Richards WD, Gamst AC, Persson KA, Ceder G (2016) The thermodynamic scale of inorganic crystalline metastability. Sci Adv 2:e1600225
Togo A, Tanaka I (2018) Spglib: a software library for crystal symmetry search. ArXiv e-prints: 1808.01590. http://adsabs.harvard.edu/abs/2018arXiv180801590T
Tran R, Xu Z, Radhakrishnan B, Winston D, Sun W, Persson KA, Ong SP (2016) Surface energies of elemental crystals. Sci Data 3:160080. https://doi.org/10.1038/sdata.2016.80, http://www.nature.com/doifinder/10.1038/cgt.2016.38, http://www.nature.com/articles/sdata201680
Van Rossum G et al (2007) Python programming language. In: USENIX annual technical conference, vol 41, p 36
Wang L, Maxisch T, Ceder G (2006) Oxidation energies of transition metal oxides within the GGA + U framework. Phys Rev B 73:195107. https://link.aps.org/doi/10.1103/PhysRevB.73.195107
Winston D, Mathew K, Montoya J, Huck P, Dwaraknath S, Dagdelen J, Liu M, Horton M, Jain A (2017) Assets for the 2017 materials project workshop. https://doi.org/10.5281/zenodo.1040436
Yan Q, Yu J, Suram SK, Zhou L, Shinde A, Newhouse PF, Chen W, Li G, Persson KA, Gregoire JM, Neaton JB (2017) Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment. Proc Nat Acad Sci 114(12):3040–3043. https://doi.org/10.1073/pnas.1619940114
Zhou F, Cococcioni M, Marianetti CA, Morgan D, Ceder G (2004) First-principles prediction of redox potentials in transition-metal compounds with LDA + u. Phys Rev B 70:235121. https://link.aps.org/doi/10.1103/PhysRevB.70.235121
Zimmermann NER, Horton MK, Jain A, Haranczyk M (2017) Assessing local structure motifs using order parameters for motif recognition, interstitial identification, and diffusion path characterization. Front Mater 4:34
Acknowledgements
We thank Professor Gerbrand Ceder, who cofounded the Materials Project and contributed to many of the ideas presented here. We also thank past and present contributors to the Materials Project and the worldwide community of developers that collaborate on the various software libraries that are instrumental to the project.
The Materials Project is funded by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC02-05-CH11231: Materials Project program KC23MP.
We thank the National Energy Research Supercomputing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231, for providing the primary source of supercomputing time as well as web portal hosting and support. We also thank the San Diego Supercomputing Center for providing additional computing.
Finally, we thank the MP user community for providing feedback and inspiration for the project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this entry
Cite this entry
Jain, A. et al. (2018). The Materials Project: Accelerating Materials Design Through Theory-Driven Data and Tools. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling . Springer, Cham. https://doi.org/10.1007/978-3-319-42913-7_60-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-42913-7_60-1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42913-7
Online ISBN: 978-3-319-42913-7
eBook Packages: Springer Reference Physics & AstronomyReference Module Physical and Materials Science