Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
- DOI
- 10.1186/s13321-022-00652-1
- Published
- 2022-10-17
- Container
- Journal of Cheminformatics
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s13321-022-00652-1,
title = {Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules},
author = {Jong Youl Choi and Pei Zhang and Kshitij Mehta and Andrew Blanchard and Massimiliano Lupo Pasini},
year = {2022},
journal = {Journal of Cheminformatics},
doi = {10.1186/s13321-022-00652-1},
url = {https://doi.org/10.1186/s13321-022-00652-1}
}RIS
TY - JOUR TI - Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules AU - Jong Youl Choi AU - Pei Zhang AU - Kshitij Mehta AU - Andrew Blanchard AU - Massimiliano Lupo Pasini PY - 2022 JO - Journal of Cheminformatics DO - 10.1186/s13321-022-00652-1 UR - https://doi.org/10.1186/s13321-022-00652-1 ER -
APA
Choi, J. Y., Zhang, P., Mehta, K., Blanchard, A., & Pasini, M. L. (2022). Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. Journal of Cheminformatics. https://doi.org/10.1186/s13321-022-00652-1
Source records
- crossref · retrieved 2026-09-25T22:12:02.600Z