The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Multi-fidelity machine learning models for accurate bandgap predictions of solids
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Cites
Special points for Brillouin-zone integrations
Monkhorst, H. J., & Pack, J. D. (1976) · Physical Review B · doi:10.1103/physrevb.13.5188 · Cited by 71,721
Physics of Semiconductor Devices
Sze, S. M., & Ng, K. K. (2006) · doi:10.1002/0470068329 · Cited by 14,627
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911
Rajan, K. (2005) · Materials Today · doi:10.1016/s1369-7021(05)71123-8 · Cited by 539
Machine learning bandgaps of double perovskites
Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., & Lookman, T. (2016) · Scientific Reports · doi:10.1038/srep19375 · Cited by 518
Self-citation
Cited by
Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,607
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Self-citationLookman, T., Balachandran, P. V., Xue, D., & Yuan, R. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0153-8 · Cited by 832
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
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Jablonka, K. M., Ongari, D., Moosavi, S. M., & Smit, B. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00004 · Cited by 599
Metadata
- Authors
- Pilania, G., Gubernatis, J. E., & Lookman, T.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 129
- Pages
- 156-163
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W2563751252
- Cited by
- 342
- References
- 6
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Pilania, G., Gubernatis, J. E., & Lookman, T. (2017). Multi-fidelity machine learning models for accurate bandgap predictions of solids. Computational Materials Science. https://doi.org/10.1016/j.commatsci.2016.12.004
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
