A quantitative uncertainty metric controls error in neural network-driven chemical discovery
This journal is © The Royal Society of Chemistry. Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale chemic...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Royal Society of Chemistry (RSC)
2021
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| Online Access: | https://hdl.handle.net/1721.1/134631 |