A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules
- DOI
- 10.1021/acs.jctc.5c01520
- Published
- 2025-11-26
- Container
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jctc.5c01520,
title = {A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules},
author = {Charlie Adams and Joshua T. Horton and Lily Wang and Simon Boothroyd and David L. Mobley and David W. Wright and Daniel J. Cole},
year = {2025},
journal = {Journal of Chemical Theory and Computation},
doi = {10.1021/acs.jctc.5c01520},
url = {https://doi.org/10.1021/acs.jctc.5c01520}
}RIS
TY - JOUR TI - A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules AU - Charlie Adams AU - Joshua T. Horton AU - Lily Wang AU - Simon Boothroyd AU - David L. Mobley AU - David W. Wright AU - Daniel J. Cole PY - 2025 JO - Journal of Chemical Theory and Computation DO - 10.1021/acs.jctc.5c01520 UR - https://doi.org/10.1021/acs.jctc.5c01520 ER -
APA
Adams, C., Horton, J. T., Wang, L., Boothroyd, S., Mobley, D. L., Wright, D. W., & Cole, D. J. (2025). A Graph Neural Network Charge Model Targeting Accurate Electrostatic Properties of Organic Molecules. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.5c01520
Source records
- crossref · retrieved 2026-09-26T11:40:20.342Z