Automated thermodynamic database development within the CALPHAD method using pycalphad
For the direct parameter generation, it's unlikely that it will be supported because the ionic liquid requires doing the energy minimization step to determine the site ratios of the cation and anion sublattices which allow the liquid to charge balance.
On the other hand, because the ionic liquid model is supported by pycalphad, you can use ESPEI to try to optimize the parameter values via MCMC if you supply a parameterized model with reasonable starting values
pycalphad doesn't support the ionic solid model yet (we had a closed PR that attempted to address it here: pycalphad/pycalphad#222). We have some solver changes upcoming that might make it worth addressing again.
I think that it would be possible to support parameter generation for ionic soilds, but that's not a high priority on our current roadmap for ESPEI and it would be pending support for ionic solids from pycalphad.
When I try to fit using MCMC I get some error. Here is how I started (datasets are stored in a folder called datasets):
- espei --check-datasets datasets: Finishes with no errors
- espei --input espei-in.yml: Finishes with no errors
where espei-in.yml looks like this:
system:
phase_models: mgsnX_input.json
datasets: datasets
generate_parameters:
excess_model: linear
ref_state: SGTE91
output:
output_db: mgsn.tdb
- espei --input mcmc.yml: This command causes errors
where mcmc.yml looks like this
system:
phase_models: mgsnX_input.json
datasets: datasets
mcmc:
iterations: 1000
input_db: mgsn.tdb
output:
output_db: mgsn_mcmc.tdb
I get the following error:
/home/davidkleiven/.local/lib/python3.7/site-packages/pycalphad/codegen/callables.py:97: UserWarning: State variables in build_callables are not {N, P, T}, but {T, P}. This can lead to incorrectly calculated values if the state variables used to call the generated functions do not match the state variables used to create them. State variables can be added with the additional_statevars argument.
"additional_statevars argument.".format(state_variables))
Traceback (most recent call last):
File "/home/davidkleiven/.local/bin/espei", line 11, in <module>
load_entry_point('espei', 'console_scripts', 'espei')()
File "/home/davidkleiven/Documents/ESPEI/espei/espei_script.py", line 311, in main
run_espei(input_settings)
File "/home/davidkleiven/Documents/ESPEI/espei/espei_script.py", line 260, in run_espei
approximate_equilibrium=approximate_equilibrium,
File "/home/davidkleiven/Documents/ESPEI/espei/optimizers/opt_base.py", line 36, in fit
node = self._fit(symbols, datasets, args, kwargs)
File "/home/davidkleiven/Documents/ESPEI/espei/optimizers/opt_mcmc.py", line 238, in _fit
self.predict(initial_guess, ctx)
File "/home/davidkleiven/Documents/ESPEI/espei/optimizers/opt_mcmc.py", line 305, in predict
non_eq_thermochemical_prob = calculate_non_equilibrium_thermochemical_probability(parameters=np.array(params), *non_equilibrium_thermochemical_kwargs)
File "/home/davidkleiven/Documents/ESPEI/espei/error_functions/non_equilibrium_thermochemical_error.py", line 271, in calculate_non_equilibrium_thermochemical_probability
points=data['calculate_dict']['points'])[output]
File "/home/davidkleiven/Documents/ESPEI/espei/shadowfunctions.py", line 55, in calculate
largest_energy=float(1e10), fake_points=fp)
File "/home/davidkleiven/.local/lib/python3.7/site-packages/pycalphad/core/calculate.py", line 190, in _compute_phase_values
param_symbols, parameter_array = extract_parameters(parameters)
File "/home/davidkleiven/.local/lib/python3.7/site-packages/pycalphad/core/utils.py", line 361, in extract_parameters
parameter_array_lengths = set(np.atleast_1d(val).size for val in parameters.values())
AttributeError: 'NoneType' object has no attribute 'values
has anyone seen this before or have can point me in the right direction? Thanks in advance for your help.
@zhanghaihui_gitlab ESPEI computes the log-likelihood for every type of data by finding value of the log probability density function of a normal distribution centered at zero with a standard deviation of σ, i.e. norm(loc=0, scale=σ).logpdf(error) where error is the difference between the expected and calculated value for a particular set of parameters.
Since we have multiple types of data, a default value of the σ_initial exists for all supported data types to handle the different scales of error and to make the likelihoods comparable even though the errors are not. The data_weights modifies the σ used in the normal distribution by making σ = σ_initial/data_weight[data_type] for each data_type like ZPF or HM. By default, all data_weight values are 1.0, i.e. σ = σ_initial.
For example: consider that we have two experiments: one for enthalpy of formation data and one for entropy of formation data. If we calculate the error as the difference between the expected enthalpy of formation and the calculated enthalpy of formation, we might find the difference is 10 J/mol. If we do the same for the entropy of formation, we might find that the error is 5 J/K-mol.
Now we need to determine the likelihood for these two errors, one with a difference between the expected/calculated value of 10 and another of 5. Using our existing knowledge about the relative magnitudes of formation enthalpy vs. entropy, we know that an error of 10 J/mol in the enthalpy of formation is very close to the solution (i.e. high likelihood), while an error of 5 J/K-mol in the enthalpy of formation is probably not that close to the solution (i.e. low likelihood). The σ_initial are set so that the likelihoods of these two cases are comparable, but you might not agree with my choice of σ_initial or you might want to value a particular type of data more heavily than I do. data_weights lets you do that.
data_weights. I can update the docs with an edited version of this to help answer this question in the future
3 replies
bocklund/zpf-misicbility-gap branch that's open in ESPEI#151, Tobias Spitaler and I just recently put together a new script that uses some refactoring to get at these driving forces a little easier. Here's the code example with some images: https://github.com/PhasesResearchLab/ESPEI/pull/151/#issuecomment-817177546
output.verbosity: 3 can be insightful too, but there's a significant amount of output