The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The NOMAD laboratory: from data sharing to artificial intelligence
Open accessAbstract
The Novel Materials Discovery (NOMAD) Laboratory is a user-driven platform for sharing and exploiting computational materials science data. It accounts for the various aspects of data being a crucial raw material and most relevant to accelerate materials research and engineering. NOMAD, with the NOMAD Repository, and its code-independent and normalized form, the NOMAD Archive, comprises the worldwide largest data collection of this field. Based on its findable accessible, interoperable, reusable data infrastructure, various services are offered, comprising advanced visualization, the NOMAD Encyclopedia, and artificial-intelligence tools. The latter are realized in the NOMAD Analytics Toolkit. Prerequisite for all this is the NOMAD metadata, a unique and thorough description of the data, that are produced by all important computer codes of the community. Uploaded data are tagged by a persistent identifier, and users can also request a digital object identifier to make data citable. Developments and advancements of parsers and metadata are organized jointly with users and code developers. In this work, we review the NOMAD concept and implementation, highlight its orthogonality to and synergistic interplay with other data collections, and provide an outlook regarding ongoing and future developments.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Computational approaches championed by the Materials Project, OQMD, AFLOWLIB and NOMAD have used density functional theory as approximations of physical energies.
“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 [21]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572
Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., & Ghiringhelli, L. M. (2018) · Physical Review Materials · doi:10.1103/physrevmaterials.2.083802 · Cited by 944
Self-citation
Flagged first, then by citations
Research works printed but not linked: 23
- Draxl, 2019, Big-data-driven materials science and its FAIR data infrastructure, 10.1007/978-3-319-42913-7_104-1 · DOI
- Tanaka, 2018, MRS Bull., 43, 662, 10.1557/mrs.2018.205 · DOI
- 2017, Nature, 546, 327
- Draxl, 2017, Nature, 548, 523, 10.1038/548523d · DOI
- Ghiringhelli, 2017, npj Comput. Mater., 3, 46, 10.1038/s41524-017-0048-5 · DOI
- Curtarolo, 2012, Comput. Mater. Sci., 58, 218, 10.1016/j.commatsci.2012.02.005 · DOI
- Calderon, 2015, Comput. Mater. Sci., 108, 233, 10.1016/j.commatsci.2015.07.019 · DOI
- Jain, 2018, The materials project: accelerating materials design through theory-driven data and tools, 10.1007/978-3-319-42913-7_60-1 · DOI
- Draxl, 2018, MRS Bull., 43, 676, 10.1557/mrs.2018.208 · DOI
- Saad, 2012, Phys. Rev. B, 85, 10.1103/PhysRevB.85.104104 · DOI
- Ghiringhelli, 2015, Phys. Rev. Lett., 114, 10.1103/PhysRevLett.114.105503 · DOI · Paper page
- Ghiringhelli, 2017, New J. Phys., 19, 10.1088/1367-2630/aa57bf · DOI
- Goldsmith, 2017, New J. Phys., 19, 10.1088/1367-2630/aa57c2 · DOI
- Glielmo, 2017, Phys. Rev. B, 95, 10.1103/PhysRevB.95.214302 · DOI
- Ouyang, 2018, Phys. Rev. Mater., 2
- Lambert, 2018, Comput. Phys. Commun., 232, 256, 10.1016/j.cpc.2018.04.029 · DOI
- Ziletti, 2018, Nat. Commun., 9, 2775, 10.1038/s41467-018-05169-6 · DOI
- Bartel, 2019, Sci Adv., 5, 10.1126/sciadv.aav0693 · DOI
- Sutton, 2019, npj Comput. Mater.
- Acosta, 2019, Nat. Mater.
- Broder, 1997, Comput. Netw. ISDN Syst., 29, 1157, 10.1016/S0169-7552(97)00031-7 · DOI
- Ke, 2017, Adv. Neural Inf. Process. Syst., 30, 3146
- Saal, 2013, JOM, 65, 1501, 10.1007/s11837-013-0755-4 · DOI · Paper page
Cited by
Sorted by citations
Scaling deep learning for materials discovery
Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023) · Nature · doi:10.1038/s41586-023-06735-9 · Cited by 1,527
Jacobsson, T. J., Hultqvist, A., García-Fernández, A., Anand, A., Al-Ashouri, A., Hagfeldt, A., Crovetto, A., Abate, A., Ricciardulli, A. G., Vijayan, A., et al. (2021) · Nature Energy · doi:10.1038/s41560-021-00941-3 · Cited by 372
Metadata
- Authors
- Draxl, C., & Scheffler, M.
- Affiliations
- Max Planck Society (DE)
- Corresponding author
- Draxl, Claudia
- Scheffler, Matthias
- Venue
- Journal of Physics: Materials
- Publisher
- IOP Publishing
- Published
- 2019-05-13
- Volume
- 2
- Issue
- 3
- Pages
- 036001
- ISSN
- 2515-7639 (ISSN-L)
- OpenAlex
- W2924281192
- Cited by
- 388
- References
- 4
- Citations by year
- 2026: 59 · 2025: 58 · 2024: 81 · 2023: 51 · 2022: 50 · 2021: 52 · 2020: 30 · 2019: 6
- FWCI
- 11.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Physics Materials (publishedVersion, cc-by)
- edoc Publication server (Humboldt University of Berlin) (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- edoc Publication server (Humboldt University of Berlin) (cc-by)
- Funders
- Horizon 2020 Framework Programme (676580)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- nomadic peoples, computational materials science, data sharing, data interoperability, metadata, persistent identifiers, AI tools, data visualization, data reuse
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
