The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A polymer dataset for accelerated property prediction and design
Open accessAbstract
Emerging computation- and data-driven approaches are particularly useful for rationally designing materials with targeted properties. Generally, these approaches rely on identifying structure-property relationships by learning from a dataset of sufficiently large number of relevant materials. The learned information can then be used to predict the properties of materials not already in the dataset, thus accelerating the materials design. Herein, we develop a dataset of 1,073 polymers and related materials and make it available at http://khazana.uconn.edu/. This dataset is uniformly prepared using first-principles calculations with structures obtained either from other sources or by using structure search methods. Because the immediate target of this work is to assist the design of high dielectric constant polymers, it is initially designed to include the optimized structures, atomization energies, band gaps, and dielectric constants. It will be progressively expanded by accumulating new materials and including additional properties calculated for the optimized structures provided.
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Cites
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
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2013) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,534
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
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966
Reymond, J.-L. (2015) · Accounts of Chemical Research · doi:10.1021/ar500432k · Cited by 720
Cited by
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-citationFeng, Q.-K., Zhong, S.-L., Pei, J.-Y., Zhao, Y., Zhang, D.-L., Liu, D.-F., Zhang, Y.-X., & Dang, Z.-M. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00793 · Cited by 744
Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions
Kim, C., Chandrasekaran, A., Huan, T. D., Das, D., & Ramprasad, R. (2018) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.8b02913 · Cited by 513
Self-citationWu, S., Kondo, Y., Kakimoto, M.-A., Yang, B., Yamada, H., Kuwajima, I., Lambard, G., Hongo, K., Xu, Y., Shiomi, J., et al. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0203-2 · Cited by 478
Relationships between structural complexity, coral traits, and reef fish assemblages
Darling, E. S., Graham, N. A. J., Januchowski-Hartley, F. A., Nash, K. L., Pratchett, M. S., & Wilson, S. K. (2017) · Coral Reefs · doi:10.1007/s00338-017-1539-z · Cited by 363
Metadata
- Authors
- Huan, T. D., Mannodi-Kanakkithodi, A., Kim, C., Sharma, V., Pilania, G., & Ramprasad, R.
- Venue
- Scientific Data
- Published
- 2016-03-01
- Volume
- 3
- Issue
- 1
- Pages
- 160012-160012
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W2291908932
- Cited by
- 217
- References
- 5
- Type
- data paper
- Language
- en
- Open access
- gold
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