Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the molecular representation becomes a critical ingredient in ML...
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| Format: | Article |
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American Chemical Society (ACS)
2020
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| Online Access: | https://hdl.handle.net/1721.1/123835 |