subject Articles
Machine Learning of Accurate Energy-conserving Molecular Force Fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R., Science Advances, 3(5), 2017, e1603015.
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
Chmiela, S., Sauceda, H. E., Müller, K.-R., Tkatchenko, A., Nature Communications, 9(1), 2018, 3887.
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., Tkatchenko, A., Computer Physics Communications, 240, 2019, 38-45.
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
Sauceda, H. E., Chmiela, S., Poltavsky, I., Müller, K.-R., Tkatchenko, A., The Journal of Chemical Physics, 150, 2019, 114102.
code Code (latest: v0.3.2)
Replicate our numerical results or reconstruct a force field from your own dataset with a Python implementation of sGDML.
Test a pre-trained model on one of the published datasets
1. Download the aspirin model & dataset.
$ sgdml-get model aspirin
$ sgdml-get dataset aspirin_dft2. Test the model on 10k data points.
$ sgdml test aspirin.npz aspirin_dft.npz 10000
You can find more examples here: sGDML documentation
view_agenda Datasets
| Name | Size | Download |
DFT [FHI-aims, light tier 1] |
||
| Benzene (from Chmiela et al., 2018) | 49 863 | |
| Benzene (from Chmiela et al., 2017) | 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 | 211 762 | |
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 | |