Bulk methane models and simulation parameters
MetadataShow full item record
Veit, M. (2018). Bulk methane models and simulation parameters [Dataset]. https://doi.org/10.17863/CAM.26364
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.
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.
methane, quantum nuclear effects, machine learning
Related research output: https://doi.org/10.1021/acs.jctc.8b01242
This record's DOI: https://doi.org/10.17863/CAM.26364
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Licence URL: https://creativecommons.org/licenses/by-sa/4.0/