Editorial: Driving Innovation in Organic Optoelectronic Materials With Physics-Based and Machine-Learning De Novo Methods.
- DOI
- 10.3389/fchem.2022.973254
- Published
- 2022
- Container
- Frontiers in chemistry
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3389/fchem.2022.973254,
title = {Editorial: Driving Innovation in Organic Optoelectronic Materials With Physics-Based and Machine-Learning De Novo Methods.},
author = {Winget P and Halls MD and Gómez-Bombarelli R and Adachi C},
year = {2022},
journal = {Frontiers in chemistry},
doi = {10.3389/fchem.2022.973254},
url = {https://doi.org/10.3389/fchem.2022.973254}
}RIS
TY - JOUR TI - Editorial: Driving Innovation in Organic Optoelectronic Materials With Physics-Based and Machine-Learning De Novo Methods. AU - Winget P AU - Halls MD AU - Gómez-Bombarelli R AU - Adachi C PY - 2022 JO - Frontiers in chemistry DO - 10.3389/fchem.2022.973254 UR - https://doi.org/10.3389/fchem.2022.973254 ER -
APA
P, W., MD, H., R, G., & C, A. (2022). Editorial: Driving Innovation in Organic Optoelectronic Materials With Physics-Based and Machine-Learning De Novo Methods.. Frontiers in chemistry. https://doi.org/10.3389/fchem.2022.973254
Source records
- pubmed · retrieved 2026-09-25T23:43:58.968Z