The DOI matches its Crossref record; 180 of the 180 references it cites were found in a registry or an index.
Structure prediction drives materials discovery
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Cites
Computer Simulation of Liquids
Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
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Crystal structure prediction via particle-swarm optimization
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Synthesis of borophenes: Anisotropic, two-dimensional boron polymorphs
Mannix, A. J., Zhou, X.-F., Kiraly, B., Wood, J. D., Alducin, D., Myers, B. D., Liu, X., Fisher, B. L., Santiago, U., Guest, J. R., et al. (2015) · Science · doi:10.1126/science.aad1080 · Cited by 2,744
Self-citationPólya, G. (1945) · Cited by 2,602
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
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Cited by
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Csányi, G., & Ceriotti, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00021 · Cited by 523
A generative model for inorganic materials design
Zeni, C., Pinsler, R., Zügner, D., Fowler, A., Horton, M., Fu, X., Wang, Z., Shysheya, A., Crabbé, J., Ueda, S., et al. (2025) · Nature · doi:10.1038/s41586-025-08628-5 · Cited by 518
Generative Adversarial Networks for Crystal Structure Prediction
Kim, S., Noh, J., Gu, G. H., Aspuru-Guzik, A., & Jung, Y. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00426 · Cited by 265
Graph convolutional neural networks with global attention for improved materials property prediction
Louis, S.-Y., Zhao, Y., Nasiri, A., Wang, X., Song, Y., Liu, F., & Hu, J. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp01474e · Cited by 257
Electronic-structure methods for materials design
Marzari, N., Ferretti, A., & Wolverton, C. (2021) · Nature Materials · doi:10.1038/s41563-021-01013-3 · Cited by 253
Metadata
- Authors
- Oganov, A. R., Pickard, C. J., Zhu, Q., & Needs, R. J.
- Venue
- Nature Reviews Materials
- Published
- 2019-04-04
- Volume
- 4
- Issue
- 5
- Pages
- 331-348
- ISSN
- 2058-8437 (ISSN-L)
- OpenAlex
- W2933118606
- Cited by
- 717
- References
- 180
- FWCI
- 3.64
- Type
- journal article
- Language
- en
- Open access
- green
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- Oganov, A. R., Pickard, C. J., Zhu, Q., & Needs, R. J. (2019). Structure prediction drives materials discovery. Nature Reviews Materials. https://doi.org/10.1038/s41578-019-0101-8
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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.
