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
NumPy Extended XYZ
Benzene (from Chmiela et al., 2017) 627,983
NumPy Extended XYZ
Uracil 133,770
NumPy Extended XYZ
Naphthalene 326,250
NumPy Extended XYZ
Aspirin 211,762
NumPy Extended XYZ
Salicylic acid 320,231
NumPy Extended XYZ
Malonaldehyde 993,237
NumPy Extended XYZ
Ethanol 555,092
NumPy Extended XYZ
Toluene 442,790
NumPy Extended XYZ
NEW Paracetamol 106,490
NumPy Extended XYZ
NEW Azobenzene 211,762
NumPy Extended XYZ
CCSD
NEW Aspirin 1,500
NumPy Extended XYZ
CCSD(T)
NEW Benzene 1,500
NumPy Extended XYZ
NEW Malonaldehyde 1,500
NumPy Extended XYZ
NEW Ethanol 2,000
NumPy Extended XYZ
NEW Toluene 1,500
NumPy Extended XYZ