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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Open accessAbstract
Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design.
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“Instead, computational approaches championed by the Materials Project (MP)16, the Open Quantum Materials Database (OQMD)17, AFLOWLIB20 and NOMAD21 have used first-principles calculations based on density functional theory (DFT) as approximations of physical energies.”
From Scaling deep learning for materials discovery (2023) · cites this paper as [16]
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“while further structural details of more than 2000 of these new compounds have been placed in the GNoME Explorer archive3 within the Materials Project.4”
From Artificial Intelligence Driving Materials Discovery? Perspective on the Article: Scaling Deep Learning for Materials Discovery (2024) · cites this paper as [4]
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“Assuming future advances in materials science, ML research could potentially draw upon open databases such as the Materials Project [380] and the UCI Machine Learning Repository [271] to invent new, climate-friendly materials [830].”
From Tackling climate change with machine learning (2023) · cites this paper as [380]
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Cites
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 76,871
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541
Density-Functional Theory for Time-Dependent Systems
Runge, E., & Gross, E. K. U. (1984) · Physical Review Letters · doi:10.1103/physrevlett.52.997 · Cited by 8,914
Accurate Band Gaps of Semiconductors and Insulators with a Semilocal Exchange-Correlation Potential
Tran, F., & Blaha, P. (2009) · Physical Review Letters · doi:10.1103/physrevlett.102.226401 · Cited by 6,135
Hedin, L. (1965) · Physical Review · doi:10.1103/physrev.139.a796 · Cited by 5,561
Van der Waals density functionals applied to solids
Klimeš, J., Bowler, D. R., & Michaelides, A. (2011) · Physical Review B · doi:10.1103/physrevb.83.195131 · Cited by 4,624
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
Self-citationComputational high-throughput screening of electrocatalytic materials for hydrogen evolution
Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I., & Nørskov, J. K. (2006) · Nature Materials · doi:10.1038/nmat1752 · Cited by 4,097
Flagged first, then by citations
Research works printed but not linked: 3
- U.S.D. of Energy, 2010, Department of Energy Workshop: Computational Materials Science and Chemistry for Innovation
- 2012, Proceedings of the 5th Workshop on Many-Task Computing on Grids and Supercomputers (MTAGS)
- 1984, Russ. Metall., 2, 202
Cited by
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Items 801–900 of 1,040
Target‐Driven Design of Deep‐UV Nonlinear Optical Materials via Interpretable Machine Learning
Wu, M., Tikhonov, E., Tudi, A., Kruglov, I. A., Hou, X., Xie, C., Pan, S., & Yang, Z. (2023) · Advanced Materials · doi:10.1002/adma.202300848 · Cited by 125
A cost-effective all-in-one halide material for all-solid-state batteries
Fu, J., Wang, C., Wang, S., et al. (2025) · Nature · doi:10.1038/s41586-025-09153-1 · Cited by 125
He, X., & Mo, Y. (2015) · Physical Chemistry Chemical Physics · doi:10.1039/c5cp02181b · Cited by 124
Hernández, F. A., & Pereslavtsev, P. E. (2018) · Fusion Engineering and Design · doi:10.1016/j.fusengdes.2018.09.014 · Cited by 124
Particle analogs of electrons in colloidal crystals
Girard, M., Wang, S., Du, J. S., et al. (2019) · Science · doi:10.1126/science.aaw8237 · Cited by 124
Automated generation and ensemble-learned matching of X-ray absorption spectra
Zheng, C., Mathew, K., Chen, C., Chen, Y., Tang, H., Dozier, A., Kas, J. J., Vila, F. D., Rehr, J. J., Piper, L. F. J., et al. (2018) · npj Computational Materials · doi:10.1038/s41524-018-0067-x · Cited by 125
Self-citationKingsbury, R., Gupta, A. S., Bartel, C. J., Munro, J. M., Dwaraknath, S., Horton, M. K., & Persson, K. A. (2022) · Physical Review Materials · doi:10.1103/physrevmaterials.6.013801 · Cited by 129
Self-citationQuadratic and cubic nodal lines stabilized by crystalline symmetry
Yu, Z., Wu, W., Sheng, X., Zhao, Y. X., & Yang, S. A. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.99.121106 · Cited by 127
Sturluson, Á., Huynh, M. T., Kaija, A. R., Laird, C., Yoon, S., Hou, F., Feng, Z., Wilmer, C. E., Colón, Y. J., Chung, Y. G., et al. (2019) · Molecular Simulation · doi:10.1080/08927022.2019.1648809 · Cited by 128
Zhang, Y., Tian, Y., Xiao, Y., Miara, L. J., Aihara, Y., Tsujimura, T., Shi, T., Scott, M. C., & Ceder, G. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903778 · Cited by 123
Self-citationLan, J., Palizhati, A., Shuaibi, M., Wood, B. M., Wander, B., Das, A., Uyttendaele, M., Zitnick, C. L., & Ulissi, Z. W. (2023) · npj Computational Materials · doi:10.1038/s41524-023-01121-5 · Cited by 147
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Zhou, Y., Zhang, W., Ma, E., & Deringer, V. L. (2023) · Nature Electronics · doi:10.1038/s41928-023-01030-x · Cited by 130
Sharp, I. D., Cooper, J. K., Toma, F. M., et al. (2016) · ACS Energy Letters · doi:10.1021/acsenergylett.6b00586 · Cited by 122
Machine learning reveals orbital interaction in materials
Pham, T.-L., Kino, H., Terakura, K., Miyake, T., Tsuda, K., Takigawa, I., & Dam, H. (2017) · Science and Technology of Advanced Materials · doi:10.1080/14686996.2017.1378060 · Cited by 129
Use machine learning to find energy materials
Luna, P. D., Wei, J. N., Bengio, Y., Aspuru‐Guzik, A., & Sargent, E. H. (2017) · Nature · doi:10.1038/d41586-017-07820-6 · Cited by 121
Yi, J., Chen, J., Zheng, Y., et al. (2019) · Advanced Energy Materials · doi:10.1002/aenm.201901796 · Cited by 122
Townsend, J., Micucci, C. P., Hymel, J. H., Maroulas, V., & Vogiatzis, K. D. (2020) · Nature Communications · doi:10.1038/s41467-020-17035-5 · Cited by 133
Effect of oxygen vacancies on ceria catalyst for selective catalytic reduction of NO with NH3
Zhang, B., Zhang, S., & Liu, B. (2020) · Applied Surface Science · doi:10.1016/j.apsusc.2020.147068 · Cited by 122
Munro, J. M., Latimer, K., Horton, M. K., et al. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00383-7 · Cited by 122
Self-citationPredicting electronic structure properties of transition metal complexes with neural networks
Janet, J. P., & Kulik, H. J. (2017) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 134
Synthetic accessibility and stability rules of NASICONs
Ouyang, B., Wang, J., He, T., Bartel, C. J., Huo, H., Wang, Y. E., Lacivita, V., Kim, H., & Ceder, G. (2021) · Nature Communications · doi:10.1038/s41467-021-26006-3 · Cited by 122
Self-citationMachine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts
Wang, M., & Zhu, H. (2021) · ACS Catalysis · doi:10.1021/acscatal.1c00178 · Cited by 124
Machine learning for design, phase transformation and mechanical properties of alloys
Durodola, J. F. (2021) · Progress in Materials Science · doi:10.1016/j.pmatsci.2021.100797 · Cited by 122
Yang, Z. K., & Gao, W. (2022) · Advanced Science · doi:10.1002/advs.202106043 · Cited by 125
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Menon, D., & Ranganathan, R. (2022) · ACS Omega · doi:10.1021/acsomega.2c03264 · Cited by 135
Tsounis, C., Kumar, P. V., Masood, H., Kulkarni, R. P., Gautam, G. S., Müller, C. R., Amal, R., & Kuznetsov, D. A. (2022) · Angewandte Chemie International Edition · doi:10.1002/anie.202210828 · Cited by 125
Designing All-Solid-State Batteries by Theoretical Computation: A Review
Zhang, S., Ma, J., Dong, S., & Cui, G. (2023) · Electrochemical Energy Reviews · doi:10.1007/s41918-022-00143-9 · Cited by 124
“The Materials Genome Initiative (MGI) have developed computational materials databases such as the Materials Project (MP) [28], Automatic Flow (AFLOW) for materials discovery [29], and the Open Quantum Materials Database (OQMD) [30] based on DFT calculations, along with other crystallographic…”
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Chen, H., Zheng, Y., Li, J., Li, L., & Wang, X. (2023) · ACS Nano · doi:10.1021/acsnano.3c01062 · Cited by 130
Exploiting redundancy in large materials datasets for efficient machine learning with less data
Li, K., Persaud, D., Choudhary, K. K., DeCost, B., Greenwood, M., & Hattrick-Simpers, J. R. (2023) · Nature Communications · doi:10.1038/s41467-023-42992-y · Cited by 134
“To ensure a comprehensive and robust assessment of data redundancy, we examine the formation energy, band gap, and bulk modulus data in three widely-used DFT databases, namely JARVIS 15 , Materials Project (MP) 16 , and OQMD 17 .”
Progress toward Solid State Synthesis by Design
Chamorro, J. R., & McQueen, T. M. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00382 · Cited by 126
The role of decomposition reactions in assessing first-principles predictions of solid stability
Bartel, C. J., Weimer, A. W., Lany, S., Musgrave, C. B., & Holder, A. M. (2018) · npj Computational Materials · doi:10.1038/s41524-018-0143-2 · Cited by 121
Predicting superhard materials via a machine learning informed evolutionary structure search
Avery, P., Wang, X., Oses, C., Gossett, E., Proserpio, D. M., Toher, C., Stefano Curtarolo, A., & Zurek, E. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0226-8 · Cited by 121
Al‐Qaisi, S., Raics, P., Haq, B. U., et al. (2020) · Materials Chemistry and Physics · doi:10.1016/j.matchemphys.2020.123945 · Cited by 121
Breuck, P.-P. D., Hautier, G., & Rignanese, G. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00552-2 · Cited by 135
Self-citationEr-Doping Enhances the Oxygen Evolution Performance of Cobalt Oxide in Acidic Medium
Pan, S., Li, H., Wang, T., Fu, Y., Wang, S., Xie, Z., Li, W., Li, H., & Li, N. (2024) · ACS Catalysis · doi:10.1021/acscatal.4c03088 · Cited by 125
Artificial intelligence for materials discovery
Gomes, C. P., Selman, B., & Gregoire, J. M. (2019) · MRS Bulletin · doi:10.1557/mrs.2019.158 · Cited by 119
Ionic Transport in Potential Coating Materials for Mg Batteries
Chen, T., Gautam, G. S., & Canepa, P. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b02692 · Cited by 121
Schwalbe, J. A., Statt, M. J., Chosy, C., et al. (2020) · ChemElectroChem · doi:10.1002/celc.201902124 · Cited by 120
Inorganic Halide Double Perovskites with Optoelectronic Properties Modulated by Sublattice Mixing
Bartel, C. J., Clary, J. M., Sutton, C. A., Vigil‐Fowler, D., Goldsmith, B. R., Holder, A. M., & Musgrave, C. B. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.9b12440 · Cited by 122
Jin, D., Johnson, L. R., Raman, A. S., Ming, X., Gao, Y. P., Du, F., Wei, Y., Chen, G., Vojvodić, A., Gogotsi, Y. G., et al. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c01460 · Cited by 124
Chen, Y., Tian, Y., Zhou, Y., Fang, D., Ding, X., Sun, J. F., & Xue, D. (2020) · Journal of Alloys and Compounds · doi:10.1016/j.jallcom.2020.156159 · Cited by 119
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
Xie, Y., Sattari, K., Zhang, C., & Lin, J. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.101043 · Cited by 129
Improving machine-learning models in materials science through large datasets
Schmidt, J., Cerqueira, T. F. T., Romero, A. H., Loew, A., Jäger, F., Wang, H., Botti, S., & Marques, M. A. L. (2024) · Materials Today Physics · doi:10.1016/j.mtphys.2024.101560 · Cited by 157
Skelton, J. M., Tiana, D., Parker, S. C., Togo, A., Tanaka, I., & Walsh, A. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4928058 · Cited by 119
A high-throughput framework for determining adsorption energies on solid surfaces
Montoya, J. H., & Persson, K. A. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0017-z · Cited by 122
Self-citationPerspective on ab initio phonon thermal transport
Lindsay, L., Katre, A., Cepellotti, A., & Mingo, N. (2019) · Journal of Applied Physics · doi:10.1063/1.5108651 · Cited by 119
Screening stable and metastable ABO3 perovskites using machine learning and the materials project
Liu, H., Cheng, J., Dong, H., Feng, J., Pang, B., Tian, Z., Ma, S., Xia, F., Zhang, C., & Dong, L. (2020) · Computational Materials Science · doi:10.1016/j.commatsci.2020.109614 · Cited by 120
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Sherrell, P. C., Fronzi, M., Shepelin, N. A., Corletto, A., Winkler, D. A., Ford, M. J., Shapter, J. G., & Ellis, A. V. (2021) · Chemical Society Reviews · doi:10.1039/d1cs00844g · Cited by 120
Guo, W., Zhang, S., Zhang, J., Wu, H., Ma, Y., Song, Y. M., Cheng, L., Chang, L., Li, G., Liu, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43303-1 · Cited by 124
“Initial structures of CuO and Cu 2 O were obtained from the Materials Project database based on our XRD and TEM results 52 .”
Qi, J., Ko, T. W., Wood, B. C., Pham, T. A., & Ong, S. P. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01227-4 · Cited by 120
“…supercells of crystalline and grain boundary TiH n (0 ≤ n ≤ 2) structures at 300, 1000, and 3000 K, and a 3 × 3 × 2 supercell of the most stable H 2 structure in MP (P6 45 , while the P6/mmm Ti is a polymorph with comparable formation energy as thatoftheknownHCPTiaccordingtotheMaterialsProject 36 .”
BackgroundMethodInfluentialSelf-citationChallenges in High-Throughput Inorganic Materials Prediction and Autonomous Synthesis
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Zaia, E. W., Gordon, M. P., Yuan, P., & Urban, J. J. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201800823 · Cited by 121
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MethodInfluentialMi, W., Luo, K., Trickey, S. B., & Pavanello, M. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00758 · Cited by 120
Focassio, B., Freitas, L. P. M., & Schleder, G. R. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c03815 · Cited by 140
“The Materials Project surface dataset (crystalium) [26] was accessed on Feb. 2023 through Materials Project’s API [51].”
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Bristow, J. K., Tiana, D., & Walsh, A. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500515h · Cited by 119
Jalem, R., Kanamori, K., Takeuchi, I., Nakayama, M., Yamasaki, H., & Saito, T. (2018) · Scientific Reports · doi:10.1038/s41598-018-23852-y · Cited by 121
Kim, J.-H., Kim, S. J., Yuk, T., et al. (2018) · ACS Energy Letters · doi:10.1021/acsenergylett.8b02043 · Cited by 117
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Qiao, J., Amachraa, M., Моlokeev, М. S., et al. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b02990 · Cited by 117
Self-citationChee, S. W., Arce-Ramos, J. M., Li, W., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16027-9 · Cited by 117
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Tang, B., Jaschin, P. W., Li, X., Bo, S., & Zhou, Z. P. (2020) · Materials Today · doi:10.1016/j.mattod.2020.08.016 · Cited by 117
Chen, Y., Sun, Y., Wang, M., Wang, J., Li, H., Xi, S., Wei, C., Xi, P., Sterbinsky, G. E., Freeland, J. W., et al. (2021) · Science Advances · doi:10.1126/sciadv.abk1788 · Cited by 120
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Back, S., Kim, J., Kim, Y., & Jung, Y. (2016) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6b05903 · Cited by 117
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Aykol, M., Hegde, V. I., Hung, L., Suram, S., Herring, P., Wolverton, C., & Hummelshøj, J. S. (2019) · Nature Communications · doi:10.1038/s41467-019-10030-5 · Cited by 120
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Galiffi, E., Carini, G., Ni, X., Álvarez‐Pérez, G., Yves, S., Renzi, E. M., Nolen, R., Waßerroth, S., Wolf, M., Alonso‐González, P., et al. (2023) · Nature Reviews Materials · doi:10.1038/s41578-023-00620-7 · Cited by 122
Nehan, P. Z. Z., Vitayaya, O., Munazat, D. R., et al. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp01077a · Cited by 116
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Aykol, M., & Wolverton, C. M. (2014) · Physical Review B · doi:10.1103/physrevb.90.115105 · Cited by 117
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Andersen, C. W., Armiento, R., Blokhin, E., Conduit, G. J., Dwaraknath, S., Evans, M. L., Fekete, Á., Gopakumar, A., Gražulis, S., Merkys, A., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-00974-z · Cited by 147
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Kanai, S., Heremans, F. J., Seo, H., et al. (2022) · arXiv (Cornell University) · doi:10.5281/zenodo.6323097 · Cited by 116
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Hendon, C. H., Yang, R., Burton, L. A., & Walsh, A. (2014) · Journal of Materials Chemistry A · doi:10.1039/c4ta05284f · Cited by 114
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Borysov, S. S., Geilhufe, R. M., & Balatsky, A. V. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171501 · Cited by 114
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Noh, J., Back, S., Kim, J., & Jung, Y. (2018) · Chemical Science · doi:10.1039/c7sc03422a · Cited by 121
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Qi, J., Banerjee, S., Zuo, Y., Chen, C., Zhu, Z., Chandrappa, M. L. H., Li, X., & Ong, S. P. (2021) · Materials Today Physics · doi:10.1016/j.mtphys.2021.100463 · Cited by 114
“̊ 3, an energy cutoff of 520 eV and an electronic relaxation convergence condition of 5 10 5 eV/atom, which were consistent with those used in MP [60].”
MethodSelf-citationMachine Learning in Screening High Performance Electrocatalysts for CO2 Reduction
Zhang, N., Yang, B., Liu, K., Li, H., Chen, G., Qiu, X., Li, W., Hu, J., Fu, J., Jiang, Y., et al. (2021) · Small Methods · doi:10.1002/smtd.202100987 · Cited by 114
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Metadata
- Authors
- Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., & Persson, K. A.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- ORCID
- Venue
- APL Materials
- Publisher
- AIP Publishing
- Published
- 2013-07-01
- Volume
- 1
- Issue
- 1
- ISSN
- 2166-532X (ISSN-L)
- OpenAlex
- W1992985800
- Semantic Scholar
- 98476502ec663771d41d2f8c948fd176257f17bd
- Cited by
- 13,094
- References
- 59
- Citations by year
- 2026: 1877 · 2025: 2248 · 2024: 2086 · 2023: 1534 · 2022: 1425 · 2021: 1182 · 2020: 902 · 2019: 619 · 2018: 462 · 2017: 308 · 2016: 235 · 2015: 145 · 2014: 64 · 2013: 4
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- APL Materials (publishedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Digital Access to Libraries (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Energy (-AC02-05CH11231, DEFG0296ER45571, DE‐FG02‐, 05CH11231, DE AC02 05CH11231, FG0296ER45571, DE-AC02-)
- National Energy Research Scientific Computing Center
- European Commission (321988, DE-AC02-05CH11231)
- Fonds De La Recherche Scientifique - FNRS
- Basic Energy Sciences (DE-AC02, DE-AC02-05CH11231., DE-FG02, AC02–05CH11231)
- Lawrence Berkeley National Laboratory (AC02‐05CH11231, DE-AC02-05CH11231, 05CH11231)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- Materials Project, Materials Genome Initiative, inorganic materials, high-throughput computing, materials property prediction, materials discovery
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