Bulk methane models and simulation parameters

Change log

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.

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.
methane, quantum nuclear effects, machine learning
EPSRC (1602415)