Here are some small Python functions that you can use to fit Joback parameters from other models such as DIPPR eq. 107 (see here for English site) . I did not test the functions but hopefully it's a good starting point. I am sure there are correlations for heat capacities for R134a and R245fa available somewhere.

import numpy as np
from scipy.optimize import least_squares
from feos.si import *

def dippr_eq_107(temperature, parameters):
    """DIPPR equation number 107.  
    
    Parameters
    ----------
    temperature : SINumber
        Temperature. 
    parameters : np.ndarray
        Parameter vector.

    Returns
    -------
    SINumber : c_p^ig
    """
    t = temperature / KELVIN
    return (
        parameters[0] 
        + parameters[1] * (parameters[2] / t / np.sinh(parameters[2] / t))**2 
        + parameters[3] * (parameters[4] / t / np.cosh(parameters[4] / t))**2
    ) * JOULE/MOL/KELVIN

def cp_ig_joback(temperature, parameters):
    """Ideal gas heat capacity using Joback's method. 
    
    Parameters
    ----------
    temperature : SINumber
        Temperature. 
    parameters : np.ndarray
        Parameter vector.

    Returns
    -------
    SINumber : c_p^ig
    """
    t = temperature / KELVIN
    a, b, c, d = parameters
    return (
        a - 37.93 
        + (b + 0.21)* t 
        + (c - 3.91e-4) * t**2 
        + (d + 2.06e-7) * t**3
    ) * JOULE/MOL/KELVIN

def cost(parameters, dippr_parameters, temperatures):
    """Cost is relative deviation."""
    dippr = dippr_eq_107(temperatures, dippr_parameters)
    joback = cp_ig_joback(temperatures, parameters)
    return (dippr - joback) / dippr

temperatures = np.arange(300.0, 500.0) * KELVIN # the T-range of the c_p model
x0 = np.array([...]) # initial guess
dippr_parameters = np.array([...]) # parameters for the substance 
result = least_squares(fun=cost, x0=x0, args=(dippr_parameters, temperatures), verbose=2)

Uh oh!

The specific heat capacity of an ideal gas #156

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