The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Inverse design in search of materials with target functionalities
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Citations
Cites
Quantum Spin Hall Insulator State in HgTe Quantum Wells
König, M., Wiedmann, S., Brüne, C., Roth, A., Buhmann, H., Molenkamp, L. W., Qi, X.-L., & Zhang, S.-C. (2007) · Science · doi:10.1126/science.1148047 · Cited by 6,151
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The high-throughput highway to computational materials design
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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
Machine learning bandgaps of double perovskites
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Cited by
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
“Physics-based deep learning can also aid in inverse design problems, a challenging but important task 84,85 .”
BackgroundFrom DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
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
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617
Opportunities and Challenges for Machine Learning in Materials Science
Morgan, D., & Jacobs, R. (2020) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010015 · Cited by 549
A critical examination of compound stability predictions from machine-learned formation energies
Bartel, C. J., Trewartha, A., Wang, Q., Dunn, A., Jain, A., & Ceder, G. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00362-y · Cited by 277
How machine learning can help select capping layers to suppress perovskite degradation
Hartono, N. T. P., Thapa, J., Tiihonen, A., Oviedo, F., Batali, C., Yoo, J. J., Liu, Z., Li, R., Marrón, D. F., Bawendi, M. G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17945-4 · Cited by 189
Artificial intelligence-driven approaches for materials design and discovery
Cheng, M., Fu, C.-L., Okabe, R., Chotrattanapituk, A., Boonkird, A., Hung, N. T., & Li, M. (2026) · Nature Materials · doi:10.1038/s41563-025-02403-7 · Cited by 109
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
Generative AI for crystal structures: A review
De Breuck, P.-P., Wang, H.-C., Rignanese, G.-M., Botti, S., & Marques, M. A. L. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01881-2 · Cited by 24
A review on machine learning-guided design of energy materials
Kim, S., Xu, J., Shang, W., Xu, Z., Lee, E., & Luo, T. (2024) · Progress in Energy · doi:10.1088/2516-1083/ad7220 · Cited by 19
Metadata
- Authors
- Zunger, A.
- Venue
- Nature Reviews Chemistry
- Published
- 2018-03-29
- Volume
- 2
- Issue
- 4
- ISSN
- 2397-3358 (ISSN-L)
- OpenAlex
- W2794979247
- Cited by
- 513
- References
- 6
- FWCI
- 1.42
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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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.
