ensemble (TI_RS)

This keyword is used to set up a nonequilibrium thermodynamic integration integrator with Reversible Scaling (RS) path. It calculates Gibbs free energy along an isobaric path. Please check [Koning2001] and [Freitas2016] for more details.


The parameters can be specified as follows:

ensemble ti_rs temp <tmin> <tmax> tperiod <tau_temperature> <pressure_control> <pressure> pperiod <tau_pressure> tswitch <switch_time> tequil <equilibrium_time>
  • <tmin> and <tmax>: The temperature range of the RS simulation.

  • <pressure_control> and <pressure>: The pressure of RS simualtion. Please refer to MTTK ensemble for more information.

  • <tau_temperature> and <tau_pressure>: These parameters are optional. Please refer to MTTK ensemble for more information.

  • <equilibrium_time>: The number timesteps to equilibrate the system.

  • <switch_time>: The number timesteps to vary lambda.

If you do not specify <equilibrium_time> and <switch_time>, they will be automatically set in a 1:4 ratio.


ensemble ti_rs temp 300 3000 aniso 10 tswitch 10000 tequil 1000

This command switch lambda for 10000 timesteps.

Output file

This command will produce a csv file. The columns are lambda, dlambda, and enthalpy (eV/atom).

The following code can help you calculate the Gibbs free energy:

import yaml
import numpy as np
import matplotlib.pyplot as plt
from pandas import read_csv
from scipy.integrate import cumtrapz
from ase.units import kB

# you need to run a ti_spring simulation at t_min
with open("ti_spring_300.yaml", "r") as f:
    y =  yaml.safe_load(f)

T0 = y["T"]
G0 = y["G"]

rs = read_csv("ti_rs.csv")
n = int(len(rs)/2)
forward = rs[:n]
backward = rs[n:][::-1]
dl = forward["dlambda"]
l = forward["lambda"]
H1 = forward["enthalpy"]
H2 = backward["enthalpy"]
T = T0/l

w = (cumtrapz(H1,l,initial=0) + cumtrapz(H2,l,initial=0))*0.5

G = (G0 + 1.5*kB*T0*np.log(l) + w)/l
plt.plot(T, G, label="RS")
plt.xlabel("T (K)")
plt.ylabel("G (eV/atom)")