The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
Open accessAbstract
Traditional machine learning (ML) metrics overestimate model performance for materials discovery.
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Cites
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Ridge regression: Biased estimation for nonorthogonal problems
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Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J. J., Schröder, B., Thuiller, W., et al. (2017) · Ecography · doi:10.1111/ecog.02881 · Cited by 3,689
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Martin, J. H., Yahata, B. D., Hundley, J. M., Mayer, J. A., Schaedler, T. A., & Pollock, T. M. (2017) · Nature · doi:10.1038/nature23894 · Cited by 2,950
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16
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A general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831
Self-citationDeep Neural Nets as a Method for Quantitative Structure–Activity Relationships
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Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O., & Roitberg, A. E. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5023802 · Cited by 811
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
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
“ML/DL models are prone to perform poorly on extrapolation 289 .”
BackgroundMethodData‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808
A 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
- Meredig, B., Antono, E., Church, C., Hutchinson, M., Ling, J., Paradiso, S., Blaiszik, B., Foster, I., Gibbons, B., Hattrick-Simpers, J., Mehta, A., & Ward, L.
- Venue
- Molecular Systems Design & Engineering
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2018
- Volume
- 3
- Issue
- 5
- Pages
- 819-825
- ISSN
- 2058-9689 (ISSN-L)
- OpenAlex
- W2885048850
- Cited by
- 264
- References
- 30
- FWCI
- 7.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- CoChat. (2026). Citation check: Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. https://app.cochat.ai/papers/10.1039/c8me00012c
- The paper
- Meredig, B., Antono, E., Church, C., Hutchinson, M., Ling, J., Paradiso, S., Blaiszik, B., Foster, I., Gibbons, B., Hattrick-Simpers, J., Mehta, A., & Ward, L. (2018). Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery. Molecular Systems Design & Engineering. https://doi.org/10.1039/c8me00012c
Data sources
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.
