The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Machine Learning Strategy for Accelerated Design of Polymer Dielectrics
Open accessAbstract
The ability to efficiently design new and advanced dielectric polymers is hampered by the lack of sufficient, reliable data on wide polymer chemical spaces and the difficulty of generating such data given time and computational/experimental constraints. Here, we address the issue of accelerating polymer dielectrics design by extracting learning models from data generated by accurate state-of-the-art first principles computations for polymers occupying an important part of the chemical subspace. The polymers are ‘fingerprinted’ as simple, easily attainable numerical representations, which are mapped to the properties of interest using a machine learning algorithm to develop an on-demand property prediction model. Further, a genetic algorithm is utilised to optimise polymer constituent blocks in an evolutionary manner, thus directly leading to the design of polymers with given target properties. While this philosophy of learning to make instant predictions and design is demonstrated here for the example of polymer dielectrics, it is equally applicable to other classes of materials as well.
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Machine learning in materials informatics: recent applications and prospects
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Self-citationFrom DFT to machine learning: recent approaches to materials science–a review
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Predicting the state of charge and health of batteries using data-driven machine learning
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Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions
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Metadata
- Authors
- Mannodi-Kanakkithodi, A., Pilania, G., Huan, T. D., Lookman, T., & Ramprasad, R.
- Venue
- Scientific Reports
- Published
- 2016-02-15
- Volume
- 6
- Issue
- 1
- Pages
- 20952-20952
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2415372084
- Cited by
- 389
- References
- 44
- FWCI
- 15.86
- Type
- journal article
- Language
- en
- Open access
- gold
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