The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Accelerated search for materials with targeted properties by adaptive design
Open accessAbstract
Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (Δ T) NiTi-based shape memory alloys, with Ti 50.0 Ni 46.7 Cu 0.8 Fe 2.3 Pd 0.2 possessing the smallest Δ T(1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller Δ T than any of the 22 in the original data set.
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
Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
Special points for Brillouin-zone integrations
Monkhorst, H. J., & Pack, J. D. (1976) · Physical Review B · doi:10.1103/physrevb.13.5188 · Cited by 71,721
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Efficient Global Optimization of Expensive Black-Box Functions
Jones, D. R., Schonlau, M., & Welch, W. J. (1998) · Journal of Global Optimization · doi:10.1023/a:1008306431147 · Cited by 8,070
Norm-Conserving Pseudopotentials
Hamann, D. R., Schlüter, M., & Chiang, C. (1979) · Physical Review Letters · doi:10.1103/physrevlett.43.1494 · Cited by 4,511
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
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995
Thermal Contraction and Disordering of the Al(110) Surface
Marzari, N., Vanderbilt, D., De Vita, A., & Payne, M. C. (1999) · Physical Review Letters · doi:10.1103/physrevlett.82.3296 · Cited by 1,255
Cited by
Xie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
High-entropy nanoparticles: Synthesis-structure-property relationships and data-driven discovery
Yao, Y., Dong, Q., Brozena, A., Luo, J., Miao, J., Chi, M., Wang, C., Kevrekidis, I. G., Ren, Z. J., Greeley, J., et al. (2022) · Science · doi:10.1126/science.abn3103 · Cited by 1,081
Machine learning assisted design of high entropy alloys with desired property
Wen, C., Zhang, Y., Wang, C., Xue, D., Bai, Y., Antonov, S., Dai, L., Lookman, T., & Su, Y. (2019) · Acta Materialia · doi:10.1016/j.actamat.2019.03.010 · Cited by 936
Self-citationLookman, T., Balachandran, P. V., Xue, D., & Yuan, R. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0153-8 · Cited by 832
Self-citation
Metadata
- Authors
- Xue, D., Balachandran, P. V., Hogden, J., Theiler, J., Xue, D., & Lookman, T.
- Venue
- Nature Communications
- Published
- 2016-04-15
- Volume
- 7
- Issue
- 1
- Pages
- 11241-11241
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2337110853
- Cited by
- 801
- References
- 8
- FWCI
- 24.01
- Type
- journal article
- Language
- en
- Open access
- gold
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.
