Bulk methane models and simulation parameters
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Type
Dataset
Change log
Authors
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.
Version
Software / Usage instructions
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
Publisher
Sponsorship
EPSRC (1602415)