Machine-Learning Prediction of Carrier Mobility and Polarity Type of Polymer Semiconductors: from High-Fidelity Data to Experimental Realization
- DOI
- 10.1021/jacs.6c11486
- Published
- 2026-09-18
- Container
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/jacs.6c11486,
title = {Machine-Learning Prediction
of Carrier Mobility and
Polarity Type of Polymer Semiconductors: from High-Fidelity Data to
Experimental Realization},
author = {Yuan-Kai Li and Si-Lu Li and Yi Liu and Hao-Tian Wu and Tian-Yu Zhang and Jie-Yu Wang and Ze-Fan Yao and Jian Pei},
year = {2026},
journal = {Journal of the American
Chemical Society},
doi = {10.1021/jacs.6c11486},
url = {https://doi.org/10.1021/jacs.6c11486}
}RIS
TY - JOUR TI - Machine-Learning Prediction of Carrier Mobility and Polarity Type of Polymer Semiconductors: from High-Fidelity Data to Experimental Realization AU - Yuan-Kai Li AU - Si-Lu Li AU - Yi Liu AU - Hao-Tian Wu AU - Tian-Yu Zhang AU - Jie-Yu Wang AU - Ze-Fan Yao AU - Jian Pei PY - 2026 JO - Journal of the American Chemical Society DO - 10.1021/jacs.6c11486 UR - https://doi.org/10.1021/jacs.6c11486 ER -
APA
Li, Y., Li, S., Liu, Y., Wu, H., Zhang, T., Wang, J., Yao, Z., & Pei, J. (2026). Machine-Learning Prediction of Carrier Mobility and Polarity Type of Polymer Semiconductors: from High-Fidelity Data to Experimental Realization. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.6c11486
Source records
- crossref · retrieved 2026-11-06T21:32:49.680Z