The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond
Open accessAbstract
The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning. Polymers have long suffered from a lack of data on electronic, mechanical, and dielectric properties across large chemical spaces, causing a stagnation in the set of suitable candidates for various applications. Extensive efforts over the last few years have seen the fruitful application of MGI principles toward the accelerated discovery of attractive polymer dielectrics for capacitive energy storage. Here, we review these efforts, highlighting the importance of computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models, and the creation of an online knowledgebase to guide ongoing and future polymer discovery and design. We lay special emphasis on the fingerprinting of polymers in terms of their genome or constituent atomic and molecular fragments, an idea that pays homage to the pioneers of the human genome project who identified the basic building blocks of the human DNA. By scoping the polymer genome, we present an essential roadmap for the design of polymer dielectrics, and provide future perspectives and directions for expansions to other polymer subclasses and properties.
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Self-citationMachine learning bandgaps of double perovskites
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Cited by
Matminer: An open source toolkit for materials data mining
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Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions
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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
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446
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Metadata
- Authors
- Mannodi-Kanakkithodi, A., Chandrasekaran, A., Kim, C., Huan, T. D., Pilania, G., Botu, V., & Ramprasad, R.
- Venue
- Materials Today
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 21
- Issue
- 7
- Pages
- 785-796
- ISSN
- 1369-7021 (ISSN-L)
- OpenAlex
- W2780553247
- Cited by
- 202
- References
- 6
- FWCI
- 8.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
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