Bulk methane models and simulation parameters

GAP machine learning potentials created for simulating condensed-phase bulk methane at the quantum mechanical level (M. Veit, S. K. Jain, S. Bonakala, I. Rudra, D. Hohl, G. Csányi, "Equation of State of Fluid Methane from First Principles with Machine Learning Potentials", J Chem Theory Comput (2019): https://pubs.acs.org/doi/10.1021/acs.jctc.8b01242). Simulation parameters for the NPT and PIMD MD simulations are also included, as are the quantum mechanical source data and fitting parameters.

Format

QUIP (https://github.com/libAtoms/QUIP), LAMMPS (http://lammps.sandia.gov), and i-PI (http://ipi-code.org) are required. See README files for usage instructions.

Keywords

methane, quantum nuclear effects, machine learning

Relationships

Related research output: https://doi.org/10.1021/acs.jctc.8b01242

Sponsorship

EPSRC (1602415)

Identifiers

This record's DOI: https://doi.org/10.17863/CAM.26364

Rights

Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

Licence URL: https://creativecommons.org/licenses/by-sa/4.0/

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Except where otherwise noted, this item's licence is described as Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)