The DOI matches its Crossref record.
Combined Electronic Structure and Evolutionary Search Approach to Materials Design
Open accessAbstract
We show that density functional theory calculations have reached an accuracy and speed making it possible to use them in conjunction with an evolutionary algorithm to search for materials with specific properties. The approach is illustrated by finding the most stable four component alloys out of the 192 016 possible fcc and bcc alloys that can be constructed out of 32 different metals. A number of well known and new "super alloys" are identified in this way.
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.
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
Cited by
Towards the computational design of solid catalysts
Nørskov, J. K., Bligaard, T., Rossmeisl, J., & Christensen, C. H. (2009) · Nature Chemistry · doi:10.1038/nchem.121 · Cited by 4,161
Self-citationAlloys of platinum and early transition metals as oxygen reduction electrocatalysts
Greeley, J., Stephens, I. E. L., Bondarenko, A. S., Johansson, T. P., Hansen, H. A., Jaramillo, T. F., Rossmeisl, J., Chorkendorff, I., & Nørskov, J. K. (2009) · Nature Chemistry · doi:10.1038/nchem.367 · Cited by 3,203
Self-citationCrystal structure prediction using ab initio evolutionary techniques: Principles and applications
Oganov, A. R., & Glass, C. W. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2210932 · Cited by 2,596
Thackeray, M. M., Wolverton, C., & Isaacs, E. D. (2012) · Energy & Environmental Science · doi:10.1039/c2ee21892e · Cited by 2,515
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I. E., Cepellotti, A., Pizzi, G., et al. (2018) · Nature Nanotechnology · doi:10.1038/s41565-017-0035-5 · Cited by 1,863
High-throughput electronic band structure calculations: Challenges and tools
Setyawan, W., & Curtarolo, S. (2010) · Computational Materials Science · doi:10.1016/j.commatsci.2010.05.010 · Cited by 1,692
Pseudopotentials for high-throughput DFT calculations
Garrity, K. F., Bennett, J. W., Rabe, K. M., & Vanderbilt, D. (2014) · Computational Materials Science · doi:10.1016/j.commatsci.2013.08.053 · Cited by 1,644
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808
Hart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703
Hautier, G., Ong, S. P., Jain, A., Moore, C. J., & Ceder, G. (2012) · Physical Review B · doi:10.1103/physrevb.85.155208 · Cited by 506
Genetic algorithms for computational materials discovery accelerated by machine learning
Jennings, P. C., Lysgaard, S., Hummelshøj, J. S., Vegge, T., & Bligaard, T. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0181-4 · Cited by 269
Self-citationMalacrida, P., Escudero-Escribano, M., Verdaguer-Casadevall, A., Stephens, I. E. L., & Chorkendorff, I. (2014) · Journal of Materials Chemistry A · doi:10.1039/c3ta14574c · Cited by 138
Chemical synthesis and materials discovery
Cheetham, A. K., Seshadri, R., & Wudl, F. (2022) · Nature Synthesis · doi:10.1038/s44160-022-00096-3 · Cited by 59
Metadata
- Authors
- Jóhannesson, G. H., Bligaard, T., Ruban, A. V., Skriver, H. L., Jacobsen, K. W., & Nørskov, J. K.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2002-06-10
- Volume
- 88
- Issue
- 25
- Pages
- 255506-255506
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2036061355
- Cited by
- 277
- References
- 0
- FWCI
- 5.85
- Type
- journal article
- Language
- en
- Open access
- bronze
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.