Bulk methane models and simulation parameters
Description
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/
Statistics
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IRUS guide.
Except where otherwise noted, this item's licence is described as Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)