The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Predicting the Band Gaps of Inorganic Solids by Machine Learning
Abstract
A machine-learning model is developed that can accurately predict the band gap of inorganic solids based only on composition. This method uses support vector classification to first separate metals from nonmetals, followed by quantitatively predicting the band gap of the nonmetals using support vector regression. The superb accuracy of the regression model is obtained by using a training set composed entirely of experimentally measured band gaps and utilizing only compositional descriptors. In fact, because of the unique training set of experimental data, the machine learning predicted band gaps are significantly closer to the experimentally reported values than DFT (PBE-level) calculated band gaps. Not only does this resulting tool provide the ability to accurately predict the band gap for any composition but also the versatility and speed of the prediction based only on composition will make this a great resource to screen inorganic phase space and direct the development of functional inorganic materials.
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From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
Data‐Driven Materials Science: Status, Challenges, and Perspectives
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Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
Lu, S., Zhou, Q., Ouyang, Y., Guo, Y., Li, Q., & Wang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05761-w · Cited by 725
Opportunities and Challenges for Machine Learning in Materials Science
Morgan, D., & Jacobs, R. (2020) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010015 · Cited by 549
Dunn, A., Wang, Q., Ganose, A., Dopp, D., & Jain, A. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00406-3 · Cited by 411
Metadata
- Authors
- Zhuo, Y., Mansouri Tehrani, A., & Brgoch, J.
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-03-13
- Volume
- 9
- Issue
- 7
- Pages
- 1668-1673
- ISSN
- 1948-7185 (ISSN-L)
- OpenAlex
- W2790960441
- Cited by
- 455
- References
- 41
- FWCI
- 13.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhuo, Y., Mansouri Tehrani, A., & Brgoch, J. (2018). Predicting the Band Gaps of Inorganic Solids by Machine Learning. The Journal of Physical Chemistry Letters. https://doi.org/10.1021/acs.jpclett.8b00124
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