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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| Summary: | 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 comprehensive dataset for training ML models for quantum chemistry. The QCML dataset systematically covers chemical space with small molecules consisting of up to 8 heavy atoms and includes elements from a large fraction of the periodic table, as well as different electronic states. Starting from chemical graphs, conformer search and normal mode sampling are used to generate both equilibrium and off-equilibrium 3D structures, for which various properties are calculated with semi-empirical methods (14.7 billion entries) and density functional theory (33.5 million entries). The covered properties include energies, forces, multipole moments, and other quantities, e.g., Kohn-Sham matrices. We provide a first demonstration of the utility of our dataset by training ML-based force fields on the data and applying them to run molecular dynamics simulations. |
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| ISSN: | 2052-4463 |