Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction
The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of t...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Chemical Society (ACS),
2022-01-12T19:45:48Z.
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| Subjects: | |
| Online Access: | Get fulltext |