The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Text-mined dataset of inorganic materials synthesis recipes
Open accessAbstract
Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of “codified recipes” for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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
Self-citationMachine 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
Neural Architectures for Named Entity Recognition
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1030 · Cited by 4,401
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847
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
Belsky, A., Hellenbrandt, M., Karen, V. L., & Luksch, P. (2002) · Acta Crystallographica Section B Structural Science · doi:10.1107/s0108768102006948 · Cited by 1,379
Curtarolo, S., Setyawan, W., Wang, S., Xue, J., Yang, K., Taylor, R. H., Nelson, L. J., Hart, G. L. W., Sanvito, S., Buongiorno-Nardelli, M., et al. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.02.002 · Cited by 1,148
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
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
“In the past few years, DL approaches such as LSTMs and transformer-based models have been employed to extract various categories of information [307], and in particular materials synthesis information [302, 308, 317] from text sources.”
MethodAn autonomous laboratory for the accelerated synthesis of inorganic materials
Szymanski, N. J., Rendy, B., Fei, Y., Kumar, R. E., He, T., Milsted, D., McDermott, M. J., Gallant, M., Cubuk, E. D., Merchant, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06734-w · Cited by 1,041
“We first removed all compositions that appeared in SynTERRA, a text-mined set of experimental synthesis data extracted from more than 24,000 publications 39 .”
Self-citationProspects for lithium-ion batteries and beyond—a 2030 vision
Grey, C. P., & Hall, D. S. (2020) · Nature Communications · doi:10.1038/s41467-020-19991-4 · Cited by 959
Structured information extraction from scientific text with large language models
Dagdelen, J., Dunn, A., Lee, S., Walker, N., Rosen, A. S., Ceder, G., Persson, K. A., & Jain, A. (2024) · Nature Communications · doi:10.1038/s41467-024-45563-x · Cited by 771
Self-citationSelf-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Metadata
- Authors
- Kononova, O., Huo, H., He, T., Rong, Z., Botari, T., Sun, W., Tshitoyan, V., & Ceder, G.
- Venue
- Scientific Data
- Published
- 2019-10-15
- Volume
- 6
- Issue
- 1
- Pages
- 203-203
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W2980932864
- Cited by
- 259
- References
- 15
- Type
- data paper
- Language
- en
- Open access
- gold
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- Kononova, O., Huo, H., He, T., Rong, Z., Botari, T., Sun, W., Tshitoyan, V., & Ceder, G. (2019). Text-mined dataset of inorganic materials synthesis recipes. Scientific Data. https://doi.org/10.1038/s41597-019-0224-1
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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