Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Copyright © 2020 American Chemical Society. Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated, unsupervised method for co...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Chemical Society (ACS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/132534 |