The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
New tolerance factor to predict the stability of perovskite oxides and halides
Open accessAbstract
Simple and interpretable data-driven descriptor accurately predicts the synthesizability of single and double perovskites.
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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
Shannon, R. D. (1976) · Acta Crystallographica Section A · doi:10.1107/s0567739476001551 · Cited by 66,145
Kim, H.-S., Lee, C.-R., Im, J.-H., Lee, K.-B., Moehl, T., Marchioro, A., Moon, S.-J., Humphry-Baker, R., Yum, J.-H., Moser, J. E., et al. (2012) · Scientific Reports · doi:10.1038/srep00591 · Cited by 8,092
Xie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
THE PRINCIPLES DETERMINING THE STRUCTURE OF COMPLEX IONIC CRYSTALS
Pauling, L. (1929) · Journal of the American Chemical Society · doi:10.1021/ja01379a006 · Cited by 2,025
Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., & Ghiringhelli, L. M. (2018) · Physical Review Materials · doi:10.1103/physrevmaterials.2.083802 · Cited by 944
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Cited by
From 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
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609
Machine learning for perovskite materials design and discovery
Tao, Q., Xu, P., Li, M., & Lu, W. (2021) · npj Computational Materials · doi:10.1038/s41524-021-00495-8 · Cited by 542
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
Metadata
- Authors
- Bartel, C. J., Sutton, C., Goldsmith, B. R., Ouyang, R., Musgrave, C. B., Ghiringhelli, L. M., & Scheffler, M.
- Venue
- Science Advances
- Published
- 2019-02
- Volume
- 5
- Issue
- 2
- Pages
- eaav0693-eaav0693
- ISSN
- 2375-2548 (ISSN-L)
- OpenAlex
- W2785060548
- Cited by
- 1,838
- References
- 7
- FWCI
- 45.42
- Type
- journal article
- Language
- en
- Open access
- gold
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- The paper
- Bartel, C. J., Sutton, C., Goldsmith, B. R., Ouyang, R., Musgrave, C. B., Ghiringhelli, L. M., & Scheffler, M. (2019). New tolerance factor to predict the stability of perovskite oxides and halides. Science Advances. https://doi.org/10.1126/sciadv.aav0693
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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.
