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.
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Cites
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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
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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
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- Xue, D., Balachandran, P. V., Hogden, J., Theiler, J., Xue, D., & Lookman, T. (2016). Accelerated search for materials with targeted properties by adaptive design. Nature Communications. https://doi.org/10.1038/ncomms11241
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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.
