YAML Input Schema

This page aims to completely describe the ESPEI input file in the YAML format. Possibly useful links are the YAML refcard and the (possibly less useful) Full YAML specification. These are all key value pairs in the format

key: value

They are nested for purely organizational purposes.

top_level_key:
  key: value

As long as keys are nested under the correct heading, they have no required order. All of the possible keys are

system:
  phase_models
  datasets
  tags

output:
  verbosity
  logfile
  output_db
  tracefile
  probfile

generate_parameters:
  excess_model
  ref_state
  ridge_alpha
  aicc_penalty_factor
  input_db
  fitting_description

mcmc:
  iterations
  prior
  save_interval
  cores
  scheduler
  input_db
  restart_trace
  chains_per_parameter
  chain_std_deviation
  deterministic
  approximate_equilibrium
  data_weights
  symbols

The next sections describe each of the keys individually. If a setting has a default of required it must be set explicitly.

system

The system key is intended to describe the specific system you are fitting, including the components, phases, and the data to fit to.

phase_models

type: string
default: required

The JSON file describing the Calphad models for each phase. See the phase models description for an example of how to write this file.

datasets

type: string
default: required

The path to a directory containing JSON files of input datasets. The file extension to each of the datasets must be named as .json, but they can otherwise be named freely.

For an examples of writing these input JSON files, see the dataset schema page.

output

verbosity

type: int
default: 0

Controls the logging level. Most users will probably want to use Info or Trace.

Warning logs should almost never occur and this log level will be relatively quiet. Debug is a fire hose of information, but may be useful in fixing calculation errors or adjusting weights.

Value	Log Level
0	Warning
1	Info
2	Trace
3	Debug

logfile

type: string
default: null

Name of the file that the logs (controlled by verbosity) will be output to. The default is None (in Python, null in YAML), meaning the logging will be output to stdout and stderr.

output_db

type: string
default: out.tdb

The database to write out. Can be any file format that can be written by a pycalphad Database.

tracefile

type: string
default: trace.npy

Name of the file that the MCMC trace is written to. The array has shape (number of chains, iterations, number of parameters).

The array is preallocated and padded with zeros, so if you selected to take 2000 MCMC iterations, but only got through 1500, the last 500 values would be all 0.

You must choose a unique file name. An error will be raised if file specified by tracefile already exists. If you don't want a file to be output (e.g. for debugging), you can enter null.

probfile

type: string
default: lnprob.npy

Name of the file that the MCMC ln probabilities are written to. The array has shape (number of chains, iterations).

The array is preallocated and padded with zeros, so if you selected to take 2000 MCMC iterations, but only got through 1500, the last 500 values would be all 0.

You must choose a unique file name. An error will be raised if file specified by probfile already exists. If you don't want a file to be output (e.g. for debugging), you can enter null.

generate_parameters

The options in generate_parameters are used to control parameter selection and fitting to single phase data. This should be used if you have input thermochemical data, such as heat capacities and mixing energies.

Generate parameters will use the Akaike information criterion to select model parameters and fit them, creating a database.

excess_model

type: string
default: required
options: linear

Which type of model to use for excess mixing parameters. Currently only linear is supported.

The exponential model is planned, as well as support for custom models.

ref_state

type: string
default: required
options: SGTE91 | SR2016

The reference state to use for the pure elements and lattice stabilities. Currently only SGTE91 and SR2016 (for certain elements) is supported.

There are plans to extend to support custom reference states.

ridge_alpha

type: float
default: 1.0e-100

Controls the ridge regression hyperparameter, \(\alpha\), as given in the following equation for the ridge regression problem

\[\min_w || Xw - y||^2_2 + \alpha ||w||^2_2\]

ridge_alpha should be a positive floating point number which scales the relative contribution of parameter magnitudes to the residuals.

If an exponential form is used, the floating point value must have a decimal place before the e, that is 1e-4 is invalid while 1.e-4 is valid. More generally, the floating point must match the following regular expression per the YAML 1.1 spec: [-+]?([0-9][0-9_]*)?\.[0-9.]*([eE][-+][0-9]+)?.

aicc_penalty_factor

type: dict
default: null

This parameter is a mapping from a phase name and property to a penalty factor to apply to the AICc number of parameters. The default is null, which means that all the penalty factors are one (1) for all phases, which means no bias for more or fewer parameters compared to the textbook definition of AICc. If phases or data are not included, the penalty factors remain one.

Increasing the penalty factor will increase the penalty for more parameters, so it will bias parameter selection to choose fewer parameters. This can be especially useful when there is not many data points and an exact fit is possible (e.g. 4 points and 4 parameters), but modeling intutition would suggest that fewer parameters are required. A negative penalty factor will bias ESPEI's parameter selection to choose more parameters, which can be useful for generating more degrees of freedom for MCMC.

aicc_penalty_factor:
  BCC_A2:
    HM: 5.0
    SM: 5.0
  LIQUID:
    HM: 2.0
    SM: 2.0

input_db

type: string
default: null

A file path that can be read as a pycalphad Database, which can provide existing parameters to add as a starting point for parameter generation, for example magnetic parameters.

If you have single phase data, ESPEI will try to fit parameters to that data regardless of whether or not parameters were passed in for that phase. You must be careful to only add initial parameters that do not have data that ESPEI will try to fit. For example, do not include liquid enthalpy of mixing data for ESPEI to fit if you are providing an initial set of parameters.

fitting_description

type: string
default: espei.parameter_selection.fitting_descriptions.gibbs_energy_fitting_description

This is a string for the fully qualified import path for a ModelFittingDescription instance. ESPEI currently provides three built-in fitting descriptions that are available to use:

# the default, fit Gibbs energy (G/L) parameters to HM/SM/CPM data:
fitting-description: espei.parameter_selection.fitting_descriptions.gibbs_energy_fitting_description
# fit molar volume (V0 and VA) parameters to V0/VM data:
fitting-description: espei.parameter_selection.fitting_descriptions.molar_volume_fitting_description
# a combination that fits volume and Gibbs energy parameters
fitting-description: espei.parameter_selection.fitting_descriptions.molar_volume_gibbs_energy_fitting_description

The ModelFittingDescription instance can be from any module that is importable from the Python environment where ESPEI is installed. For example, the Gibbs energy fitting description is importable as:

from espei.parameter_selection.fitting_descriptions import gibbs_energy_fitting_description

Because the fitting description given can be in any importable module, users can provide their own fitting descriptions that use ESPEI's built-in FittingStep objects, custom fitting steps, or a combination ESPEI's fitting steps and custom fitting steps. The Generating Custom Model Parameters tutorial gives an example of how to fit a custom PyCalphad model that uses custom parameters for BCC elastic constants.

mcmc

The options in mcmc control how Markov Chain Monte Carlo is performed using the emcee package.

In order to run an MCMC fitting, you need to specify one and only one source of parameters somewhere in the input file. The parameters can come from including a generate_parameters step, or by specifying the mcmc.input_db key with a file to load as pycalphad Database.

If you choose to use the parameters from a database, you can then further control settings based on whether it is the first MCMC run for a system (you are starting fresh) or whether you are continuing from a previous run (a 'restart').

iterations

type: int
default: required

Number of iterations to perform in emcee. Each iteration consists of accepting one step for each chain in the ensemble.

prior

type: list or dict
default: {'name': 'zero'}

Either a single prior dictionary or a list of prior dictionaries corresponding to the number of parameters. See Specifying Priors for examples and details on writing priors.

save_interval

type: int
default: 1

Controls the interval (in number of iterations) for saving the MCMC chain and probability files. By default, new files will be written out every iteration. For large files (many mcmc iterations and chains per parameter), these might become expensive to write out to disk.

cores

type: int
min: 1

How many cores from available cores to use during parallelization with dask or emcee. If the chosen number of cores is larger than available, then this value is ignored and espei defaults to using the number available.

Cores does not take affect for MPIPool scheduler option. MPIPool requires the number of processors be set directly with MPI.

scheduler

type: string
default: dask
options: dask | null | JSON file

Which scheduler to use for parallelization. You can choose from either dask, null, or pass the path to a JSON scheduler file created by dask-distributed.

Choosing dask allows for the choice of cores used through the cores key.

Choosing null will result in no parallel scheduler being used. This is useful for debugging.

Passing the path to a JSON scheduler file will use the resources set up by the scheduler. JSON file schedulers are most useful because schedulers can be started on MPI clusters using dask-mpi command. See the alternative schedulers page for more information.

input_db

type: string
default: null

A file path that can be read as a pycalphad Database. The parameters to fit will be taken from this database.

For a parameter to be fit, it must be a symbol where the name starts with VV, e.g. VV0001. For a TDB formatted database, this means that the free parameters must be functions of a single value that are used in your parameters. For example, the following is a valid symbol to fit:

FUNCTION VV0000  298.15  10000; 6000 N !

restart_trace

type: string
default: null

If you have run a previous MCMC calculation, then you will have a trace file that describes the position and history of all of the chains from the run. You can use these chains to start the emcee run and pick up from where you left off in the MCMC run by passing the trace file (e.g. chain.npy) to this key.

If you are restarting from a previous calculation, you must also specify the same database file (with input_db) as you used to run that calculation.

chains_per_parameter

type: int
default: 2

This controls the number of chains to run in the MCMC calculation as an integer multiple of the number of parameters.

This parameter can only be used when initializing the first MCMC run. If you are restarting a calculation, the number of chains per parameter is fixed by the number you chose previously.

Ensemble samplers require at least 2*p chains for p fitting parameters to be able to make proposals. If chains_per_parameter = 2, then the number of chains if there are 10 parameters to fit is 20.

The value of chains_per_parameter must be an EVEN integer.

chain_std_deviation

type: float
default: 0.1

The standard deviation to use when initializing chains in a Gaussian distribution from a set of parameters as a fraction of the parameter.

A value of 0.1 means that for parameters with values (-1.5, 2000, 50000) the chains will be initialized using those values as the mean and (0.15, 200, 5000) as standard deviations for each parameter, respectively.

This parameter can only be used when initializing the first MCMC run. If you are restarting a calculation, the standard deviation for your chains are fixed by the value you chose previously.

You may technically set this to any positive value, you would like. Be warned that too small of a standard deviation may cause convergence to a local minimum in parameter space and slow convergence, while a standard deviation that is too large may cause convergence to meaningless thermodynamic descriptions.

approximate_equilibrium

type: bool
default: False

If True, an approximate version of pycalphad's equilibrium() function will be used to calculate the driving force for phase boundary data. It uses pycalphad's starting_point to construct a approximate equilibrium hyperplanes of the lowest energy solution from a numerical sampling of each active phases's internal degrees of freedom. This can give speedups of up to 10x for calculating the ZPF likelihood, but may miss low energy solutions that are not sampled well numerically, especially for phases with many sublattices, which have low energy solutions far from the endmembers.

deterministic

type: bool
default: True

Toggles whether ESPEI runs are deterministic. If this is True, running ESPEI with the same Database and initial settings (either the same chains_per_parameter and chain_std_deviation or the same restart_trace) will result in exactly the same results.

Starting two runs with the same TDB or with parameter generation (which is deterministic) will result in the chains being at exactly the same position after 100 iterations. If these are both restarted after 100 iterations for another 50 iterations, then the final chain after 150 iterations will be the same.

It is important to note that this is only explictly True when starting at the same point. If Run 1 and Run 2 are started with the same initial parameters and Run 1 proceeds 50 iterations while Run 2 proceeds 100 iterations, restarting Run 1 for 100 iterations and Run 2 for 50 iterations (so they are both at 150 total iterations) will NOT give the same result.

data_weights

type: dict
default: {'ZPF': 1.0, 'ACR': 1.0, 'HM': 1.0, 'SM': 1.0, 'CPM': 1.0}

Each type of data can be weighted: zero phase fraction (ZPF), activity (ACR) and the different types of thermochemical error. These weights are used to modify the initial standard deviation of each data type by

\[\sigma = \frac{\sigma_{\mathrm{initial}}} {w}\]

symbols

type: list[str]
default: null

By default, any symbol in the database following the naming pattern VV#### where #### is any number is optimized by ESPEI. If this option is set, this can be used to manually fit a subset of the degrees of freedom in the system, or fit degrees of freedom that do not folow the naming convention of 'VV####':

symbols: ['VV0000', 'FF0000', ...]