NpH ensemble

The NpH ensemble (isoenthalpic–isobaric ensemble) is a statistical ensemble that is used to study material properties under the conditions of a constant particle number N, a pressure p fluctuating around an equilibrium pressure $\langle p \rangle$ and a enthalpy H fluctuating around an equilibrium value $\langle H \rangle$ . This page describes how to sample the NpH ensemble from a molecular-dynamics run.

Instructions for setting up a NpH ensemble

To run a NpH molecular-dynamics simulation the Langevin thermostat has to be used. The LANGEVIN_GAMMA and LANGEVIN_GAMMA_L have to be zero because otherwise a NpT ensemble will be simulated. By setting the tag LANGEVIN_GAMMA=0 the friction term and the stochastic term of the Langevin thermostat will be zero, such that the velocities are determined by the Hellmann-Feynman forces or Machine-learned force fields only. Setting the tag LANGEVIN_GAMMA_L=0, removes the stochastic term and the friction term from the barostat, resulting in a box update depending solely on the kinetic stress tensor.

NpH ensemble	Langevin
MDALGO	3
ISIF	3
LANGEVIN_GAMMA_L	0
LANGEVIN_GAMMA	0

It is recommended to equilibrate the system of interest with a NPT molecular-dynamics run before starting the NpH run. A general guide for molecular-dynamics simulations can be found on the molecular-dynamics page.

An example INCAR file for the NpH ensemble

 #INCAR molecular-dynamics tags NVE ensemble 
 IBRION = 0                   # choose molecular-dynamics 
 MDALGO = 3                   # using Andersen thermostat
 ISIF = 3                     # compute stress tensor but do not change box volume/shape 
 TEBEG = 300                  # set temperature 
 NSW = 10000                  # number of time steps 
 POTIM = 1.0                  # time step in femto seconds 
 LANGEVIN_GAMMA = 0.0 0.0     # setting friction and stochastic term of Langevin thermostat zero
 LANGEVIN_GAMMA_L = 0.0       # setting friction and stochastic term of Langevin barostat zero

Mind: This INCAR file only contains the parameters for the molecular-dynamics part. The electronic minimization or the machine learning tags have to be added.

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