The QCML dataset, Quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations
Abstract Machine learning (ML) methods enable prediction of the properties of chemical structures without computationally expensive ab initio calculations. The quality of such predictions depends on the reference data that was used to train the model. In this work, we introduce the QCML dataset: A c...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04720-7 |
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