The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Best practices in machine learning for chemistry
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The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
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
Self-citationXie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828
Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727
Reproducibility in density functional theory calculations of solids
Lejaeghere, K., Bihlmayer, G., Björkman, T., Blaha, P., Blügel, S., Blum, V., Caliste, D., Castelli, I. E., Clark, S. J., Dal Corso, A., et al. (2016) · Science · doi:10.1126/science.aad3000 · Cited by 1,649
Predicting reaction performance in C–N cross-coupling using machine learning
Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D., & Doyle, A. G. (2018) · Science · doi:10.1126/science.aar5169 · Cited by 1,058
Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
Sheridan, R. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400084k · Cited by 376
Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis
Jia, X., Lynch, A., Huang, Y., Danielson, M., Lang’at, I., Milder, A., Ruby, A. E., Wang, H., Friedler, S. A., Norquist, A. J., et al. (2019) · Nature · doi:10.1038/s41586-019-1540-5 · Cited by 222
Three pitfalls to avoid in machine learning.
Riley, P. (2019) · Nature · doi:10.1038/d41586-019-02307-y · Cited by 142
Cited by
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
“Testing and benchmarking methodologies such as underfitting/overfitting/cross-validation [15, 16, 37] are common knowledge, and standards for measuring model performance are well established in the community.”
MethodSelf-citationLattice oxygen redox chemistry in solid-state electrocatalysts for water oxidation
Zhang, N., & Chai, Y. (2021) · Energy & Environmental Science · doi:10.1039/d1ee01277k · Cited by 706
Shih, A. J., Monteiro, M. C. O., Dattila, F., Pavesi, D., Philips, M., da Silva, A. H. M., Vos, R. E., Ojha, K., Park, S., van der Heijden, O., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00164-0 · Cited by 521
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
Machine Learning Methods for Small Data Challenges in Molecular Science
Dou, B., Zhu, Z., Merkurjev, E., Ke, L., Chen, L., Jiang, J., Zhu, Y., Liu, J., Zhang, B., & Wei, G.-W. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00189 · Cited by 499
Metadata
- Authors
- Artrith, N., Butler, K. T., Coudert, F.-X., Han, S., Isayev, O., Jain, A., & Walsh, A.
- Venue
- Nature Chemistry
- Published
- 2021-05-31
- Volume
- 13
- Issue
- 6
- Pages
- 505-508
- ISSN
- 1755-4330 (ISSN-L)
- 1755-4349
- OpenAlex
- W3165300194
- Cited by
- 505
- References
- 9
- FWCI
- 23.79
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Artrith, N., Butler, K. T., Coudert, F.-X., Han, S., Isayev, O., Jain, A., & Walsh, A. (2021). Best practices in machine learning for chemistry. Nature Chemistry. https://doi.org/10.1038/s41557-021-00716-z
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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.
