The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Computational predictions of energy materials using density functional theory
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Cites
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
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-citationDensity-Functional Theory for Time-Dependent Systems
Runge, E., & Gross, E. K. U. (1984) · Physical Review Letters · doi:10.1103/physrevlett.52.997 · Cited by 8,914
Computational high-throughput screening of electrocatalytic materials for hydrogen evolution
Greeley, J., Jaramillo, T. F., Bonde, J., Chorkendorff, I., & Nørskov, J. K. (2006) · Nature Materials · doi:10.1038/nmat1752 · Cited by 4,097
Crystal structure prediction using ab initio evolutionary techniques: Principles and applications
Oganov, A. R., & Glass, C. W. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2210932 · Cited by 2,596
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
Large-scale screening of hypothetical metal–organic frameworks
Wilmer, C. E., Leaf, M., Lee, C. Y., Farha, O. K., Hauser, B. G., Hupp, J. T., & Snurr, R. Q. (2012) · Nature Chemistry · doi:10.1038/nchem.1192 · Cited by 1,439
Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R. H., Nelson, L. J., Hart, G. L. W., Sanvito, S., Buongiorno-Nardelli, M., et al. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.02.002 · Cited by 1,148
Hautier, G., Fischer, C. C., Jain, A., Mueller, T., & Ceder, G. (2010) · Chemistry of Materials · doi:10.1021/cm100795d · Cited by 580
Self-citationData Mined Ionic Substitutions for the Discovery of New Compounds
Hautier, G., Fischer, C., Ehrlacher, V., Jain, A., & Ceder, G. (2011) · Inorganic Chemistry · doi:10.1021/ic102031h · Cited by 394
Self-citation
Cited by
Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I. E., Cepellotti, A., Pizzi, G., et al. (2018) · Nature Nanotechnology · doi:10.1038/s41565-017-0035-5 · Cited by 1,863
High-entropy nanoparticles: Synthesis-structure-property relationships and data-driven discovery
Yao, Y., Dong, Q., Brozena, A., Luo, J., Miao, J., Chi, M., Wang, C., Kevrekidis, I. G., Ren, Z. J., Greeley, J., et al. (2022) · Science · doi:10.1126/science.abn3103 · Cited by 1,081
An autonomous laboratory for the accelerated synthesis of inorganic materials
Szymanski, N. J., Rendy, B., Fei, Y., Kumar, R. E., He, T., Milsted, D., McDermott, M. J., Gallant, M., Cubuk, E. D., Merchant, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06734-w · Cited by 1,041
“To close the gap between the rates of computational screening and experimental realization of novel materials 1,2 , we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders.”
Self-citationStructure prediction drives materials discovery
Oganov, A. R., Pickard, C. J., Zhu, Q., & Needs, R. J. (2019) · Nature Reviews Materials · doi:10.1038/s41578-019-0101-8 · Cited by 717
Efficient calculation of carrier scattering rates from first principles
Ganose, A. M., Park, J., Faghaninia, A., Woods-Robinson, R., Persson, K. A., & Jain, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22440-5 · Cited by 688
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Metadata
- Authors
- Jain, A., Shin, Y., & Persson, K. A.
- Venue
- Nature Reviews Materials
- Published
- 2016-01-11
- Volume
- 1
- Issue
- 1
- ISSN
- 2058-8437 (ISSN-L)
- OpenAlex
- W2624679515
- Cited by
- 919
- References
- 10
- FWCI
- 1.92
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jain, A., Shin, Y., & Persson, K. A. (2016). Computational predictions of energy materials using density functional theory. Nature Reviews Materials. https://doi.org/10.1038/natrevmats.2015.4
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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.
