The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Machine learning–enabled high-entropy alloy discovery
Open accessAbstract
High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10 −6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical 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
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
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
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
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512
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
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067
Finite Elastic Strain of Cubic Crystals
Birch, F. (1947) · Physical Review · doi:10.1103/physrev.71.809 · Cited by 6,571
Learning to predict by the methods of temporal differences
Sutton, R. S. (1988) · Machine Learning · doi:10.1007/bf00115009 · Cited by 5,148
Cited by
Clarifying the four core effects of high-entropy materials
Hsu, W.-L., Tsai, C.-W., Yeh, A.-C., & Yeh, J.-W. (2024) · Nature Reviews Chemistry · doi:10.1038/s41570-024-00602-5 · Cited by 928
The promise of high-entropy materials for high-performance rechargeable Li-ion and Na-ion batteries
Zheng, W., Liang, G., Liu, Q., Li, J., Yuwono, J. A., Zhang, S., Peterson, V. K., & Guo, Z. (2023) · Joule · doi:10.1016/j.joule.2023.10.016 · Cited by 279
Wang, Q., Qin, Y., Xie, J., Kong, Y., Sun, Q., Wei, Z., & Zhao, S. (2025) · Advanced Materials · doi:10.1002/adma.202420173 · Cited by 61
Language models for materials discovery and sustainability: Progress, challenges, and opportunities
Pei, Z., Yin, J., & Zhang, J. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101495 · Cited by 30
Wu, M., Sun, J., Cui, Y., Fan, L., Hong, K., Liu, W., Li, Q., Lyu, Z., & Wang, J. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202505234 · Cited by 11
Metadata
- Authors
- Rao, Z., Tung, P.-Y., Xie, R., Wei, Y., Zhang, H., Ferrari, A., Klaver, T. P. C., Körmann, F., Sukumar, P. T., Kwiatkowski da Silva, A., Chen, Y., Li, Z., Ponge, D., Neugebauer, J., Gutfleisch, O., Bauer, S., & Raabe, D.
- Venue
- Science
- Published
- 2022-10-07
- Volume
- 378
- Issue
- 6615
- Pages
- 78-85
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4302425308
- Cited by
- 745
- References
- 38
- FWCI
- 37.25
- Type
- journal article
- Language
- en
- Open access
- green
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–enabled high-entropy alloy discovery. https://app.cochat.ai/papers/10.1126/science.abo4940
- The paper
- Rao, Z., Tung, P.-Y., Xie, R., Wei, Y., Zhang, H., Ferrari, A., Klaver, T. P. C., Körmann, F., Sukumar, P. T., Kwiatkowski da Silva, A., Chen, Y., Li, Z., Ponge, D., Neugebauer, J., Gutfleisch, O., Bauer, S., & Raabe, D. (2022). Machine learning–enabled high-entropy alloy discovery. Science. https://doi.org/10.1126/science.abo4940
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.
