The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Machine learning assisted design of high entropy alloys with desired property
Open accessAbstract
Abstract We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties.
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
Yeh, J. ‐., Chen, S. ‐., Lin, S. ‐., Gan, J. ‐., Chin, T. ‐., Shun, T. ‐., Tsau, C. ‐., & Chang, S. ‐. (2004) · Advanced Engineering Materials · doi:10.1002/adem.200300567 · Cited by 15,281
Microstructural development in equiatomic multicomponent alloys
Cantor, B., Chang, I. T. H., Knight, P., & Vincent, A. J. B. (2004) · Materials Science and Engineering: A · doi:10.1016/j.msea.2003.10.257 · Cited by 10,130
A critical review of high entropy alloys and related concepts
Miracle, D. B., & Senkov, O. N. (2017) · Acta Materialia · doi:10.1016/j.actamat.2016.08.081 · Cited by 8,973
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
A fracture-resistant high-entropy alloy for cryogenic applications
Gludovatz, B., Hohenwarter, A., Catoor, D., Chang, E. H., George, E. P., & Ritchie, R. O. (2014) · Science · doi:10.1126/science.1254581 · Cited by 5,714
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · cited 1×
“4(b), from a starting set of knowledge based features, the initial decrease in error indicates a n improvement of the model and the later increase is due to possible over-fitting.[56] The best performance of the model is given by a 6-tuple feature set and what's interesti ng is that the best 3-, 4-, 5- and 6-tuple fea…”
BackgroundHigh-Entropy Alloys: A Critical Review
Tsai, M.-H., & Yeh, J.-W. (2014) · Materials Research Letters · doi:10.1080/21663831.2014.912690 · Cited by 3,477
High-entropy alloy: challenges and prospects
Ye, Y. F., Wang, Q., Lu, J., Liu, C. T., & Yang, Y. (2016) · Materials Today · doi:10.1016/j.mattod.2015.11.026 · Cited by 3,016
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
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
Cited by
High-entropy energy materials: challenges and new opportunities
Ma, Y., Ma, Y., Wang, Q., Schweidler, S., Botros, M., Fu, T., Hahn, H., Brezesinski, T., & Breitung, B. (2021) · Energy & Environmental Science · doi:10.1039/d1ee00505g · Cited by 963
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
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446
Refractory high-entropy alloys: A focused review of preparation methods and properties
Xiong, W., Guo, A. X. Y., Zhan, S., Liu, C.-T., & Cao, S. C. (2023) · Journal of Materials Science & Technology · doi:10.1016/j.jmst.2022.08.046 · Cited by 429
Metadata
- Authors
- Wen, C., Zhang, Y., Wang, C., Xue, D., Bai, Y., Antonov, S., Dai, L., Lookman, T., & Su, Y.
- Venue
- Acta Materialia
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 170
- Pages
- 109-117
- ISSN
- 1359-6454 (ISSN-L)
- OpenAlex
- W2921873493
- Cited by
- 936
- References
- 37
- FWCI
- 26.31
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Machine learning assisted design of high entropy alloys with desired property. https://app.cochat.ai/papers/10.1016/j.actamat.2019.03.010
- The paper
- Wen, C., Zhang, Y., Wang, C., Xue, D., Bai, Y., Antonov, S., Dai, L., Lookman, T., & Su, Y. (2019). Machine learning assisted design of high entropy alloys with desired property. Acta Materialia. https://doi.org/10.1016/j.actamat.2019.03.010
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.
