The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A database to enable discovery and design of piezoelectric materials
Open accessAbstract
Piezoelectric materials are used in numerous applications requiring a coupling between electrical fields and mechanical strain. Despite the technological importance of this class of materials, for only a small fraction of all inorganic compounds which display compatible crystallographic symmetry, has piezoelectricity been characterized experimentally or computationally. In this work we employ first-principles calculations based on density functional perturbation theory to compute the piezoelectric tensors for nearly a thousand compounds, thereby increasing the available data for this property by more than an order of magnitude. The results are compared to select experimental data to establish the accuracy of the calculated properties. The details of the calculations are also presented, along with a description of the format of the database developed to make these computational results publicly available. In addition, the ways in which the database can be accessed and applied in materials development efforts are described.
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Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
Self-citationPython Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
Self-citationThe 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
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Cited by
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
Metastable ferroelectricity in optically strained SrTiO 3
Nova, T. F., Disa, A. S., Fechner, M., & Cavalleri, A. (2019) · Science · doi:10.1126/science.aaw4911 · Cited by 423
de Jong, M., Chen, W., Notestine, R., Persson, K., Ceder, G., Jain, A., Asta, M., & Gamst, A. (2016) · Scientific Reports · doi:10.1038/srep34256 · Cited by 243
Self-citationBiodegradable Piezoelectric Polymers: Recent Advancements in Materials and Applications
Ali, M., Bathaei, M. J., Istif, E., Karimi, S. N. H., & Beker, L. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202300318 · Cited by 211
Application of machine learning for advanced material prediction and design
Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123
Metadata
- Authors
- de Jong, M., Chen, W., Geerlings, H., Asta, M., & Persson, K. A.
- Venue
- Scientific Data
- Published
- 2015-09-29
- Volume
- 2
- Issue
- 1
- Pages
- 150053-150053
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W2395243079
- Cited by
- 310
- References
- 4
- Type
- data paper
- Language
- en
- Open access
- gold
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- de Jong, M., Chen, W., Geerlings, H., Asta, M., & Persson, K. A. (2015). A database to enable discovery and design of piezoelectric materials. Scientific Data. https://doi.org/10.1038/sdata.2015.53
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