Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R., Science Advances, 3(5), 2017, e1603015.
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Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., Tkatchenko, A., Computer Physics Communications, 240, 2019, 38-45.
Sauceda, H. E., Chmiela, S., Poltavsky, I., Müller, K.-R., Tkatchenko, A., The Journal of Chemical Physics, 150, 2019, 114102.
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Replicate our numerical results or reconstruct a force field from your own dataset with a Python implementation of sGDML.
1. Download the aspirin model & dataset.
$ sgdml-get model aspirin
$ sgdml-get dataset aspirin_dft
2. Test the model on 10k data points.
$ sgdml test aspirin.npz aspirin_dft.npz 10000
Name | Size | Benchmark | Download |
DFT [FHI-aims, light tier 1] |
|||
Benzene (Chmiela et al., 2018) | 49 863 | ||
a.k.a. MD17 dataset (Chmiela et al., 2017) |
|||
Benzene | 627 983 | ||
Uracil | 133 770 | ||
Naphthalene | 326 250 | ||
Aspirin | 211 762 | ||
Salicylic acid | 320 231 | ||
Malonaldehyde | 993 237 | ||
Ethanol | 555 092 | ||
Toluene | 442 790 | ||
NEW Paracetamol | 106 490 | ||
NEW Azobenzene | 99 999 | ||
CCSD [Psi4, cc-pVDZ] |
|||
NEW Aspirin | 1 500 | ||
CCSD(T) [Psi4, cc-pVDZ] |
|||
NEW Benzene | 1 500 | ||
NEW Malonaldehyde | 1 500 | ||
NEW Toluene | 1 500 | ||
[Psi4, cc-pVTZ] |
|||
NEW Ethanol | 2 000 |