Graph-Based Deep Learning Models for Thermodynamic Property Prediction: The Interplay between Target Definition, Data Distribution, Featurization, and Model Architecture
- DOI
- 10.1021/acs.jcim.4c02014
- Published
- 2025-01-09
- 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.4c02014,
title = {Graph-Based Deep Learning Models for Thermodynamic Property Prediction: The Interplay between Target Definition, Data Distribution, Featurization, and Model Architecture},
author = {Bowen Deng and Thijs Stuyver},
year = {2025},
journal = {Journal of Chemical Information and Modeling},
doi = {10.1021/acs.jcim.4c02014},
url = {https://doi.org/10.1021/acs.jcim.4c02014}
}RIS
TY - JOUR TI - Graph-Based Deep Learning Models for Thermodynamic Property Prediction: The Interplay between Target Definition, Data Distribution, Featurization, and Model Architecture AU - Bowen Deng AU - Thijs Stuyver PY - 2025 JO - Journal of Chemical Information and Modeling DO - 10.1021/acs.jcim.4c02014 UR - https://doi.org/10.1021/acs.jcim.4c02014 ER -
APA
Deng, B., & Stuyver, T. (2025). Graph-Based Deep Learning Models for Thermodynamic Property Prediction: The Interplay between Target Definition, Data Distribution, Featurization, and Model Architecture. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.4c02014
Source records
- crossref · retrieved 2026-09-25T20:23:43.819Z