Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
High-throughput computational screening for chemical discovery mandates the automated and unsupervised simulation of thousands of new molecules and materials. In challenging materials spaces, such as open shell transition metal chemistry, characterization requires time-consuming first-principles sim...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Chemical Society (ACS)
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/1721.1/128282 |