The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Rational Co‐Design of Polymer Dielectrics for Energy Storage
Abstract
Although traditional materials discovery has historically benefited from intuition‐driven experimental approaches and serendipity, computational strategies have risen in prominence and proven to be a powerful complement to experiments in the modern materials research environment. It is illustrated here how one may harness a rational co‐design approach—involving synergies between high‐throughput computational screening and experimental synthesis and testing—with the example of polymer dielectrics design for electrostatic energy storage applications. Recent co‐design efforts that can potentially enable going beyond present‐day “standard” polymer dielectrics (such as biaxially oriented polypropylene) are highlighted. These efforts have led to the identification of several new organic polymer dielectrics within known generic polymer subclasses (e.g., polyurea, polythiourea, polyimide), and the recognition of the untapped potential inherent in entirely new and unanticipated chemical subspaces offered by organometallic polymers. The challenges that remain and the need for additional methodological developments necessary to further strengthen the co‐design concept are then presented.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Belsky, A., Hellenbrandt, M., Karen, V. L., & Luksch, P. (2002) · Acta Crystallographica Section B Structural Science · doi:10.1107/s0108768102006948 · Cited by 1,379
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-citationData‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808
Feng, 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-citationEmerging 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
Self-citation
Metadata
- Authors
- Mannodi‐Kanakkithodi, A., Treich, G. M., Huan, T. D., Ma, R., Tefferi, M., Cao, Y., Sotzing, G. A., & Ramprasad, R.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2016-05-11
- Volume
- 28
- Issue
- 30
- Pages
- 6277-6291
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2356584815
- Cited by
- 189
- References
- 2
- FWCI
- 9.27
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Rational Co‐Design of Polymer Dielectrics for Energy Storage. https://app.cochat.ai/papers/10.1002/adma.201600377
- The paper
- Mannodi‐Kanakkithodi, A., Treich, G. M., Huan, T. D., Ma, R., Tefferi, M., Cao, Y., Sotzing, G. A., & Ramprasad, R. (2016). Rational Co‐Design of Polymer Dielectrics for Energy Storage. Advanced Materials. https://doi.org/10.1002/adma.201600377
Data sources
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.