sGDML Symmetric Gradient Domain Machine Learning

Articles

sGDML
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

Chmiela, S., Sauceda, H. E., Müller, K.-R., Tkatchenko, A., 2018, arXiv:1802.09238.

Article BibTex

GDML
Machine Learning of Accurate Energy-conserving Molecular Force Fields

Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, Igor, Schütt, K. T., Müller, K.-R., Science Advances, 3(5), 2017, e1603015.

Article BibTex

Datasets

All geometries in Å, energy labels in kcal mol-1 and force labels in kcal mol-1 Å-1.
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
NEW Aspirin 1,500
CCSD(T)
NEW Benzene 1,500
NEW Malonaldehyde 1,500
NEW Ethanol 2,000
NEW Toluene 1,500