Abstract
SF-22-121 Graph convolutional neural networks (GCNN) are powerful machine learning models that apply a message-passing algorithm on adjacent nodes in a graph. Due to the natural representation of molecules via discrete graphs (where nodes encode atoms and edges encode bonds), GCNNs are an intuitive machine learning model chemical space informatics, including the prediction of polymer properties. Here, molecules are represented in feature and adjacency matrices where they are directly used as inputs to a GCNN.
- Developers:
- Release Date:
- 2023-03-14
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Office of Science (SC)Primary Award/Contract Number:AC02-06CH11357
- Code ID:
- 102626
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Country of Origin:
- United States
Citation Formats
FEINSTEIN, JEREMY, XU, JASON, and MATHERSON, CARLOS.
GRAPH CONVOLUTIONAL NEURAL NETWORK (GCNN)FOR POLYMER PROPERTY PREDICTION.
Computer Software.
https://github.com/AI4Plastics/AI4Plastics.
USDOE Office of Science (SC).
14 Mar. 2023.
Web.
doi:10.11578/dc.20230314.7.
FEINSTEIN, JEREMY, XU, JASON, & MATHERSON, CARLOS.
(2023, March 14).
GRAPH CONVOLUTIONAL NEURAL NETWORK (GCNN)FOR POLYMER PROPERTY PREDICTION.
[Computer software].
https://github.com/AI4Plastics/AI4Plastics.
https://doi.org/10.11578/dc.20230314.7.
FEINSTEIN, JEREMY, XU, JASON, and MATHERSON, CARLOS.
"GRAPH CONVOLUTIONAL NEURAL NETWORK (GCNN)FOR POLYMER PROPERTY PREDICTION." Computer software.
March 14, 2023.
https://github.com/AI4Plastics/AI4Plastics.
https://doi.org/10.11578/dc.20230314.7.
@misc{
doecode_102626,
title = {GRAPH CONVOLUTIONAL NEURAL NETWORK (GCNN)FOR POLYMER PROPERTY PREDICTION},
author = {FEINSTEIN, JEREMY and XU, JASON and MATHERSON, CARLOS},
abstractNote = {SF-22-121 Graph convolutional neural networks (GCNN) are powerful machine learning models that apply a message-passing algorithm on adjacent nodes in a graph. Due to the natural representation of molecules via discrete graphs (where nodes encode atoms and edges encode bonds), GCNNs are an intuitive machine learning model chemical space informatics, including the prediction of polymer properties. Here, molecules are represented in feature and adjacency matrices where they are directly used as inputs to a GCNN.},
doi = {10.11578/dc.20230314.7},
url = {https://doi.org/10.11578/dc.20230314.7},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20230314.7}},
year = {2023},
month = {mar}
}