The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
From the computer to the laboratory: materials discovery and design using first-principles calculations
Open accessAbstract
The development of new technological materials has historically been a difficult and time-consuming task. The traditional role of computation in materials design has been to better understand existing materials. However, an emerging paradigm for accelerated materials discovery is to design new compounds in silico using first-principles calculations, and then perform experiments on the computationally designed candidates. In this paper, we provide a review of ab initio computational materials design, focusing on instances in which a computational approach has been successfully applied to propose new materials of technological interest in the laboratory. Our examples include applications in renewable energy, electronic, magnetic and multiferroic materials, and catalysis, demonstrating that computationally guided materials design is a broadly applicable technique. We then discuss some of the common features and limitations of successful theoretical predictions across fields, examining the different ways in which first-principles calculations can guide the final experimental result. Finally, we present a future outlook in which we expect that new models of computational search, such as high-throughput studies, will play a greater role in guiding materials advancements.
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Cites
Density-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
Electronic Structure: Basic Theory and Practical Methods
Martin, R. M. (2004) · doi:10.1017/cbo9780511805769 · Cited by 1,752
High-throughput electronic band structure calculations: Challenges and tools
Setyawan, W., & Curtarolo, S. (2010) · Computational Materials Science · doi:10.1016/j.commatsci.2010.05.010 · Cited by 1,692
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
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Cited by
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-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
Materials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
Identification and design principles of low hole effective mass p-type transparent conducting oxides
Hautier, G., Miglio, A., Ceder, G., Rignanese, G.-M., & Gonze, X. (2013) · Nature Communications · doi:10.1038/ncomms3292 · Cited by 640
Self-citationA 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
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Metadata
- Authors
- Hautier, G., Jain, A., & Ong, S. P.
- Venue
- Journal of Materials Science
- Published
- 2012-05-10
- Volume
- 47
- Issue
- 21
- Pages
- 7317-7340
- ISSN
- 0022-2461 (ISSN-L)
- 1573-4803
- OpenAlex
- W2065905435
- Cited by
- 206
- References
- 7
- FWCI
- 5.79
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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