The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Machine learning assisted composition effective design for precipitation strengthened copper alloys
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Citations
Cites
Cullity, B. D., & Weymouth, J. W. (1957) · American Journal of Physics · doi:10.1119/1.1934486 · Cited by 17,935
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
Compendium of chemical terminology
Gold, V. (1987) · Cited by 2,525
Inverse molecular design using machine learning: Generative models for matter engineering
Sanchez-Lengeling, B., & Aspuru-Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,050
Mulliken, R. S. (1934) · The Journal of Chemical Physics · doi:10.1063/1.1749394 · Cited by 1,920
A general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831
Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727
Elements of X-Ray Diffraction.
Gregory, N. W. (1957) · Journal of the American Chemical Society · doi:10.1021/ja01564a077 · Cited by 1,685
Fracture and strength of solids
Orowan, E. (1949) · Reports on Progress in Physics · doi:10.1088/0034-4885/12/1/309 · Cited by 1,278
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
Metadata
- Authors
- Zhang, H., Fu, H., Zhu, S., Yong, W., & Xie, J.
- Venue
- Acta Materialia
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 215
- Pages
- 117118
- ISSN
- 1359-6454 (ISSN-L)
- OpenAlex
- W3175424420
- Cited by
- 259
- References
- 24
- FWCI
- 9.47
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhang, H., Fu, H., Zhu, S., Yong, W., & Xie, J. (2021). Machine learning assisted composition effective design for precipitation strengthened copper alloys. Acta Materialia. https://doi.org/10.1016/j.actamat.2021.117118
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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