Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization
- DOI
- 10.1021/acs.jcim.6c00193
- Published
- 2026-06-04
- Container
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jcim.6c00193,
title = {Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization},
author = {Yamato Nakanishi and Tatsuhito Ando and Nobuyuki N. Matsuzawa and Hiroyuki Maeshima and Hiromasa Kaneko},
year = {2026},
journal = {Journal of Chemical Information and Modeling},
doi = {10.1021/acs.jcim.6c00193},
url = {https://doi.org/10.1021/acs.jcim.6c00193}
}RIS
TY - JOUR TI - Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization AU - Yamato Nakanishi AU - Tatsuhito Ando AU - Nobuyuki N. Matsuzawa AU - Hiroyuki Maeshima AU - Hiromasa Kaneko PY - 2026 JO - Journal of Chemical Information and Modeling DO - 10.1021/acs.jcim.6c00193 UR - https://doi.org/10.1021/acs.jcim.6c00193 ER -
APA
Nakanishi, Y., Ando, T., Matsuzawa, N. N., Maeshima, H., & Kaneko, H. (2026). Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.6c00193
Source records
- crossref · retrieved 2026-09-27T09:28:32.225Z