The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Unsupervised discovery of solid-state lithium ion conductors
Open accessAbstract
Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10−4–10−1 S cm−1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.
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Cites
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G., & Furthmüller, J. (1996) · Physical Review B · doi:10.1103/physrevb.54.11169 · Cited by 123,652
From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G., & Joubert, D. (1999) · Physical Review B · doi:10.1103/physrevb.59.1758 · Cited by 85,307
Kresse, G., & Hafner, J. (1994) · Physical Review B · doi:10.1103/physrevb.49.14251 · Cited by 23,019
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
A tutorial on spectral clustering
von Luxburg, U. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,349
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
A lithium superionic conductor
Kamaya, N., Homma, K., Yamakawa, Y., Hirayama, M., Kanno, R., Yonemura, M., Kamiyama, T., Kato, Y., Hama, S., Kawamoto, K., et al. (2011) · Nature Materials · doi:10.1038/nmat3066 · Cited by 4,978
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
Python 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
Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?
Murtagh, F., & Legendre, P. (2014) · Journal of Classification · doi:10.1007/s00357-014-9161-z · Cited by 3,782
Cited by
Self-Driving Laboratories for Chemistry and Materials Science
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Artificial Intelligence Applied to Battery Research: Hype or Reality?
Lombardo, T., Duquesnoy, M., El-Bouysidy, H., Årén, F., Gallo-Bueno, A., Jørgensen, P. B., Bhowmik, A., Demortière, A., Ayerbe, E., Alcaide, F., et al. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00108 · Cited by 503
Lv, C., Zhou, X., Zhong, L., Yan, C., Srinivasan, M., Seh, Z. W., Liu, C., Pan, H., Li, S., Wen, Y., et al. (2022) · Advanced Materials · doi:10.1002/adma.202101474 · Cited by 412
Accelerating materials discovery using machine learning
Juan, Y., Dai, Y., Yang, Y., & Zhang, J. (2021) · Journal of Materials Science & Technology · doi:10.1016/j.jmst.2020.12.010 · Cited by 240
Lithium superionic conductors with corner-sharing frameworks
Jun, K., Sun, Y., Xiao, Y., Zeng, Y., Kim, R., Kim, H., Miara, L. J., Im, D., Wang, Y., & Ceder, G. (2022) · Nature Materials · doi:10.1038/s41563-022-01222-4 · Cited by 236
Metadata
- Authors
- Zhang, Y., He, X., Chen, Z., Bai, Q., Nolan, A. M., Roberts, C. A., Banerjee, D., Matsunaga, T., Mo, Y., & Ling, C.
- Venue
- Nature Communications
- Published
- 2019-11-20
- Volume
- 10
- Issue
- 1
- Pages
- 5260-5260
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2990048788
- Cited by
- 312
- References
- 56
- FWCI
- 9.47
- Type
- journal article
- Language
- en
- Open access
- gold
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- Zhang, Y., He, X., Chen, Z., Bai, Q., Nolan, A. M., Roberts, C. A., Banerjee, D., Matsunaga, T., Mo, Y., & Ling, C. (2019). Unsupervised discovery of solid-state lithium ion conductors. Nature Communications. https://doi.org/10.1038/s41467-019-13214-1
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