Machine learned prediction of reaction template applicability for data-driven retrosynthetic predictions of energetic materials
State of the art computer-aided synthesis planning models are naturally biased toward commonly reported chemical reactions, thus reducing the usefulness of those models for the unusual chemistry relevant to shock physics. To address this problem, a neural network was trained to recognize reaction te...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing
2021
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| Online Access: | https://hdl.handle.net/1721.1/137158.2 |