Molecular dynamics¶
Liquid densities¶
Summary¶
Performance in predicting densities for 61 organic liquids, each system consisting of about 1000 atoms. The dataset covers aliphatic, aromatic molecules, as well as different functional groups and halogenated molecules.
Metrics¶
Density error
For each system, the density is calculated by taking the average density of an NPT molecular dynamics run. The initial part of the simulation, here 500 ps, is omitted from the density calculation. This is compared to the reference density, obtained from experiment.
Computational cost¶
Very high: tests are likely to take several days to run on GPU.
Data availability¶
Input structures:
- Weber et al., Efficient Long-Range Machine Learning Force Fields for
Liquid and Materials Properties. arXiv:2505.06462 [physics.chem-ph]
Reference data:
Same as input data
Experimental
Water density¶
Summary¶
Performance in predicting the density of water at temperatures of 270, 290, 300, and 330 K. The water systems consist of 333 molecules.
Metrics¶
Density error
For each system, the density is calculated by taking the average density of an NPT molecular dynamics run. The initial part of the simulation, here 500 ps, is omitted from the density calculation. This is compared to the reference density, obtained from experiment.
Computational cost¶
Very high: tests are likely to take several days to run on GPU.
Data availability¶
Input structures:
Weber et al., Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties. arXiv:2505.06462 [physics.chem-ph]
Reference data:
Same as input data
Experimental