Input parameters#

sqsgenerator uses a dict-like configuration to find all the value it needs. Thus, it can it read its configuration from anything which can store Python dicts (e. g. json, yaml or pickle). However by default sqsgenerator expects an input file in YAML format.

Each of the parameters below represents an entry in the YAML (or key in a dict if called directly from Python).

Input array interpretations

A closer look on the indices of the symbols Eq. (4) reveals that those quantities need to be represented in a multidimensional manner. Those quantities are

\(p_{\xi\eta}\) or \(\tilde{p}^i_{\xi\eta}\): pair_weights
\(\tilde{\alpha}^i_{\xi\eta}\): target_objective
\(f^i_{\xi\eta}\): prefactors

Although sqsgenerator accepts scalar values as input, there might be more complex use-cases where one wants to tweak the indiviual elements/coefficients (e. g. simoultaneous multi-sublattice optimization or partial ordering).

Thus, when specifiyng 2D and 3D input values for the aforementioned parameters, please make sure that you have read through the note on array input interpretation

Parameters#

`bin_width`#

Sets the real space bin width of the histogram computed from the pair distance matrix \(r_{ij} = \left|\vec{r}_{i} - \vec{r}_j \right|\). This parameter is used in combination with the peak_isolation parameter to compute the default guess for the radii of the coordination shells. Unit is in \(\mathrm{\mathring{A}}\).

Required: No
Default: 0.05
Accepted: positive floating point number (float)

`peak_isolation`#

A threshold measure on how isolated a bin in the pair distance matrix \(r_{ij} = \left|\vec{r}_{i} - \vec{r}_j \right|\) histogram has to be, to become a separate coordination shell. An isolation 0.7 means the following. If certain bin in the histogram, for which both the left and the right neighbor are smaller than 30% as the current bin, it will become a separate coordination shell.

Required: No
Default: 0.25
Accepted: positive floating point number between 0.0 and 1.0 (float)

What are bin_width and peak_isolation used for?

bin_width and peak_isolation parameters are used to compute the default values for the shell_distances parameter.

bin_width determines the resolution of the histogram. Therefore, when passing a structure to sqsgen, that underwent ionic relaxation, it might be sensible to increase bin_width. Hence slightly distorted atoms will be put into the same coordination shell.

Have a look on the histogram and the shell_distances parameter by entering:

sqsgenerator compute shell-distances input.yaml --plot

`max_output_configurations`#

The maximum number of output configurations.

Required: No
Default: 10
Accepted: positive finite integer number (int)

Behaviour when running with MPI parallelization

In case the code runs with MPI parallelization, each MPI rank will generate at most max_output_configurations which are gathered at the head rank.

`composition`#

The composition of the output configuration, defined as a dictionary. Keys are symbols of chemical elements, whereas values are the number of atoms of the corresponding species. The number in the dict-values or the length of the specified match the number of specified positions on the sublattice. See which input parameter. The composition parameter might be also used to pin atomic species on current sublattices

Required: Yes
Accepted:
- a dictionary with chemical symbols as keys and numbers as values (dict[str, int])
- a dictionary with chemical symbols as keys and dict as values (dict[str, dict[str, int]])

Note

The sum of the atoms distributed must exactly match the number of positions on the lattice In combination with which the number must match, the amount of selected sites
If you explicitly pin atomic species on certain sublattices (see examples below) you have to specify it for all
- If you do that the number of distributed atoms must match the number of lattice positions on the specified sublattice

Examples#

Ternary alloy, consisting of 54 atoms (\(\text{Ti}_{18}\text{Al}_{18}\text{Mo}_{18}\))
```
composition:
  Ti: 18
  Al: 18
  Mo: 18
```
fcc-Aluminum cell, 64 atoms, randomly distribute 8 vacancies
```
composition:
  Al: 56
  0: 8
```
consider a \(\text{Ti}_{0.5}\text{N}_{0.5}\) supercell with 64 atoms. Let’s \((\text{Ti}_{0.25}\text{Al}_{0.25})(\text{B}_{0.25}\text{N}_{0.25})\) create an alloy from this cell. The input structure provides us with 32 lattice position of Ti and 32 positions of N. However boron will sit only on the nitrogen sites, while aluminium will most likely occupy only titanium sites. Therefore, we can constrain how sqsgenerator will distribute the atoms
```
composition: 
  Ti:
    Ti: 16 # distribute 16 Ti atoms on the Ti occupied lattice positions of the input structure
  Al:
    Ti: 16 # occupy remaining Ti sublattice positions with 16 aluminum atoms
  N: # occupy the initial 32 nitrogen sites with 16 B and 16 N atoms
    N: 16
  B:
    N: 16      
```

`which`#

Used to select a sublattice (collection of lattice sites) from the specified input structure. Note that the number of atoms in the composition parameter has to sum up to the number of selected lattice positions

Required: No
Default: all
Accepted:
- either all or a chemical symbol specified in the input structure (str)
- list of indices to choose from the input structure (list[int])

Note

The sum of the atoms distributed must exactly match the number of selected lattice positions.

Examples#

Ternary alloy, 54 atoms, create (\(\text{Ti}_{18}\text{Al}_{18}\text{Mo}_{18}\))
```
which: all
composition:
  Ti: 18
  Al: 18
  Mo: 18
```
rock-salt TiN (B1), 64 atoms, randomly distribute B and N on the N sublattice \(\text{Ti}_{32}(\text{B}_{16}\text{N}_{16}) = \text{Ti}(\text{B}_{0.5}\text{N}_{0.5})\)
```
which: N
composition:
  N: 16
  B: 16
```
rock-salt TiN (B1), 64 atoms, randomly distribute Al, V and Ti on the Ti sublattice \((\text{Ti}_{16}\text{Al}_{8}\text{V}_{8})\text{N}_{32} = (\text{Ti}_{0.5}\text{Al}_{0.25}\text{V}_{0.25})\text{N}\)
```
which: Ti
composition:
  Ti: 16
  Al: 8
  V: 8
```
select all even sites from your structure, 16 atoms, using an index, list and distribute W, Ta and Mo on those sites
```
which: [0, 2, 4, 6, 8, 10, 12, 14]
composition:
  W: 3
  Ta: 3
  Mo: 2
```

`structure`#

the structure where sqsgenerator will operate on which will select the sites from the specified structure. The coordinates must be supplied in fractional style. It can be specified by supplying a filename or directly as a dictionary

Required: Yes
Accepted:
- dictionary with a file key (dict)
- dictionary with a lattice, coords and species key (dict)

Note

If a filename is specified, and ase is available sqsgenerator will automatically use it to load the structure using ase.io.read. Alternatively it will fall back to pymatgen ( pymatgen.core.IStructure.from_file). If both packages are not available it will raise an FeatureError.
You can explicitly instruct to use one of the packages by settings structure.reader to either ase or pymatgen
You can pass custom arguments to the reader function ( ase.io.read or pymatgen.core.IStructure.from_file) by specifying structure.args (last example)

Examples#

directly specify \(\text{CsCl}\) (B2) structure in the input file

structure:  
  lattice:
    - [4.123, 0.0, 0.0]
    - [0.0, 4.123, 0.0]
    - [0.0, 0.0, 4.123]
  coords: # put fractional coordinates here -> not cartesian
    - [0.0, 0.0, 0.0]
    - [0.5, 0.5, 0.5]
  species:
    - Cs
    - Cl

Please note that for each entry in coords there must be a corresponding species specified in the species list

specify a file (must be readable by ase.io.read , fallback to pymatgen if ase is not present)
```
structure:
  file: cs-cl.vasp # POSCAR/CONTCAR format
```

specify a file and explicitly set a reader for reading the structure file

structure:
   file: cs-cl.cif
   reader: pymatgen # use pymatgen to read the CIF file

specify read a file and pass arguments to the reading package. E.g. read las configuration from an MD-trajectory
```
structure:
  file: md.traj
  reader: ase
  args:
    index: -1
```
if args is present in will be unpacked (**) into ase.io.read

`structure.supercell`#

Instructs sqsgenerator to create a supercell of the specified structure

Required: No
Accepted: a list/tuple of positive integer number of length 3 (tuple[int])

Examples#

Create a \(3\times3\times3\) supercell of the \(\text{CsCl}\) (B2) structure

structure:
  supercell: [3, 3, 3]
  lattice:
    - [4.123, 0.0, 0.0]
    - [0.0, 4.123, 0.0]
    - [0.0, 0.0, 4.123]
  coords: # put fractional coordinates here -> not cartesian
    - [0.0, 0.0, 0.0]
    - [0.5, 0.5, 0.5]
  species:
    - Cs
    - Cl

Create a \(3\times3\times3\) supercell of a structure file

structure:
  supercell:
    - 3
    - 3
    - 3
  file: cs-cl.cif

`mode`#

The iteration mode specifies how new structures are generated.

random: the configuration will be shuffled randomly
systematic: will instruct the code generate configurations in lexicographical order and to scan the complete configurational space. In case systematic is specified the iterations parameter will be ignored, since the number of permutations is predefined. Therefore, for a system with \(N\) atoms with \(M\) species, will lead to

(1)#\[N^{\text{iterations}} = \dfrac{N!}{\prod_m^M N_m!} \quad \text{where} \quad \sum_m^M N_m = N\]

Required: No
Default: random
Accepted: random or systematic (str)

How long will you calculation run?

In case you use the systematic mode make sure that the total number of configurations to check is a number a computer can check in finite times. To compute the total number of iterations you can use:

sqsgenerator compute total-permutations

sqsgenerator also allows you to guess how long it might take to check a certain number of iterations:

sqsgenerator compute estimated-time

`iterations`#

Number of configurations to check. This parameter is ignored if mode was set to systematic

Required: No
Default: \(10^5\) if mode is random
Accepted: a positive integer number (int)

`shell_distances`#

the radii of the coordination shells in Angstrom. All lattice positions will be binned into the specified coordination shells. The default distances are computed in the following way:

This is just a Python implementation which demonstrates what is happening#

shell_distances = []

# In the following atol and rtol represent the input parameters of atol and rtol

def get_closest_shell(r_ij: float) -> int:
    predicate = functools.partial(math.isclose, r_ij, abs_tol=atol, rel_tol=rtol)
    return next(filter(predicate, shell_distances), None)

# Let Rij be the distance matrix of the input structure as np.ndarray
for r_ij in sorted(Rij.flat):
    closest_shell = get_closest_shell(r_ij)
    if closest_shell: # we compute a roling average, to estimate a mean value
        shell_distances[closest_shell] = (shell_distances[closest_shell] + r_ij) / 2.0
    else: # no value similar to r_ij exists, we create a new shell
        shell_distances.append(r_ij)
    shell_distances = sorted(shell_distances)
    

Required: No
Default: automatically determined by sqsgenerator
Accepted: a list of positive floating point numbers (list[float])

Hint

You can have a look at the the computed shell distances, and check if they are fine using:

sqsgenerator params show input.yaml -p shell_distances

`shell_weights`#

accounts for the fact that coodination shells which are farther away are less important. This parameter also determines which shells should be taken into account. The shell_weights are a mapping (dictionary). It assigns the shell index to its corresponding shell weight \(w^i\). The keys represent the indices of the calculated shell distances computed or specified by the shell_distances parameter. Its values correspond to \(w^i\) (4) in the objective function.

Required: No
Default: \(\frac{1}{i}\) where \(i\) is the index of the coordination shell. Automatically determined by sqsgenerator
Accepted: a dictionary where keys are the shell indices and the values \(w^i\) parameters (dict[int, float])

Further information

If nothing is specified all available coordination shells are used
You can improve the performace of sqsgenerator by neglecting some coordination shells
If you specify a shell index which does not exist a BadSettings error will be raised
Indices which are not specified explicitly specified are not considered in the optimization
You can have a look on the generated weights by checking

sqsgenerator params show input.yaml -p shell_weights

Examples#

To consider all coordination shells, simply do not specify any value

Only use the first coordination shell
```
shell_weights:
  1: 1.0
```

Use first and second coodination shell

shell_weights:
  1: 1.0 # this are the default values anyway
  2: 0.5

`pair_weights`#

thr “pair weights” \(p_{\xi\eta}\)/\(\tilde{p}_{\xi\eta}^i\) (8) used to differentiate bonds between atomic species. Note that sqsgenerator sorts the atomic species interally in ascending order by their ordinal number. Please refer to the target_objective parameter documentation for further details regarding the internal reordering.

The default value is a hollow matrix, which is multiplied with the corresponding shell weight

(2)#\[p_{\xi\eta} = \frac{1}{2}\left(\mathbf{J}_N - \mathbf{I}_N \right)\]

where \(N=N_{\text{species}}\), \(\mathbf{J}_N\) the matrix full of ones and \(\mathbf{I}_N\) the identity matrix. Using this formalism the default value for \(\tilde{p}_{\xi\eta}^i\) is calculates according to Eq. (5) and (2) as

(3)#\[\tilde{p}_{\xi\eta}^i = w^i p_{\xi\eta} = \frac{1}{2}w_i\left(\mathbf{J}_N - \mathbf{I}_N \right)\]

where \(w^i\) is the shell_weight of the i\(^\text{th}\) coordination shell. If a 2D input or any of the sub-array in case of a 3D input array is not symmetric a BadSettings exception is raised.

Required: No
Default: an array as described in Eq. (3) shape \(\left(N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\)
Accepted:
- a 2D matrix of shape \(\left( N_{\text{species}}, N_{\text{species}} \right)\). The input is interpreted as \(p_{\xi\eta}\) and will be stacked along the first dimensions and multiplied with \(w_i\) to generate a shape of \(N_{\text{shells}}\) times to generate the \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\) array (np.ndarray)
- a 3D array of shape \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\). The input is interpreted as \(\tilde{p}_{\xi\eta}^i\) (np.ndarray)

`target_objective`#

the target objective \(\tilde{\alpha}^i_{\eta\xi}\) (4), which the SRO parameters (3) are minimzed against. It is an array of three-dimensions of shape \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\). By passing custom values you can fine-tune the individual SRO paramters.

Required: No
Default: an array of zeros of shape \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\)
Accepted:
- a single scalar value. An array filled with the scalar value of shape \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\) will be created (float)
- a 2D matrix of shape \(\left( N_{\text{species}}, N_{\text{species}} \right)\) the matrix will be stacked along the first dimension \(N_{\text{shells}}\) times to generate the \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\) array (np.ndarray)
- a 3D array of shape \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\) (np.ndarray)

A note on array input interpretation

because of \(\tilde{\alpha}^i_{\xi\eta}= \tilde{\alpha}^i_{\eta\xi}\) the target_objective is a symmetric quantity. Thus in case an \(\tilde{\alpha}_{\eta\xi}^{i,T} \neq \tilde{\alpha}_{\xi\eta}\) an BadSettings error is raised
the atomic species specified in strcuture by their ordinal number in ascending order
- In case of the \(\text{CsCl}\) the actual ordering is \(\text{Cl}(Z=17), \text{Cs}(Z=55)\).
\[\begin{split}\boldsymbol{\tilde{\alpha}}^i = \left[ \begin{array}{cc} \tilde{\alpha}_{\text{Cl-Cl}} & \tilde{\alpha}_{\text{Cl-Cs}} \\ \tilde{\alpha}_{\text{Cs-Cl}} & \tilde{\alpha}_{\text{Cs-Cs}} \end{array} \right]\end{split}\]
- In case of the \(\text{TiAlMo}\) the actual ordering is \(\text{Al}(Z=13), \text{Ti}(Z=22), \text{Mo}(Z=42)\).
\[\begin{split}\boldsymbol{\tilde{\alpha}}^i = \left[ \begin{array}{ccc} \tilde{\alpha}_{\text{Al-Al}} & \tilde{\alpha}_{\text{Al-Ti}} & \tilde{\alpha}_{\text{Al-Mo}} \\ \tilde{\alpha}_{\text{Ti-Al}} & \tilde{\alpha}_{\text{Ti-Ti}} & \tilde{\alpha}_{\text{Ti-Mo}} \\ \tilde{\alpha}_{\text{Mo-Al}} & \tilde{\alpha}_{\text{Mo-Ti}} & \tilde{\alpha}_{\text{Mo-Mo}} \\ \end{array} \right]\end{split}\]

Examples#

distribute everything randomly

target_objective: 0 # this is the default behaviour

search for a clustered \(\text{CsCl}\) structure

target_objective: 1 # which is equivalent to 
target_objective:
  - [1, 1]
  - [1, 1]

custom settings for a ternary alloy (unknown use case :smile: )

target_objective:
  - [ 1, -1, 0]
  - [-1,  1, 0]
  - [ 0,  0, 1]

`prefactors`#

The bond prefactors \(f_{\xi\eta}^i\) as defined in Eq. (6). The input representation and options are the same as for the target_objective parameter. The prefactor_mode parameter determines whether the default values are overridden or modified. One can think of the prefactors \(f^i_{\xi\eta}\) as the reciprocal value of the expected number \(\xi - \eta\) pairs in the \(i^{\text{th}}\) coordination shell

Required: No
Default: an array of zeros of shape \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\)
Accepted:
- a single scalar value. An array filled with the scalar value of shape \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\) will be created (float)
- a 2D matrix of shape \(\left( N_{\text{species}}, N_{\text{species}} \right)\) the matrix will be stacked along the first dimension \(N_{\text{shells}}\) times to generate the \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\) array (np.ndarray)
- a 3D array of shape \(\left( N_{\text{shells}}, N_{\text{species}}, N_{\text{species}} \right)\) (np.ndarray)

`prefactor_mode`#

Specifies how the prefactors parameter is interpreted

set uses the values from prefactors and interprets them directly as \(f^i_{\xi\eta}\)
mul multiplies the input array element-wise with the default values of prefactors and uses the result as \(f^i_{\xi\eta}\)
Required: No
Default: set
Accepted: set or mul (str)

`threads_per_rank`#

number of threads should be used on each rank. The value(s) is (are) passed on to omp_set_num_threads. If the version of sqsgenerator is not capable MPI parallelism, a single value is needed. If sqsgenerator was called within an MPI runtime, an entry must be present for each rank. In case OpenMP schedules a different number of threads, than specified in threads_per_rank the workload will be redistributed automatically. Negative values represent a call to omp_get_max_threads (as many threads as possible).

Required: No
Default: [-1] if there is no MPI support. Otherwise [-1]*N where N is the number of MPI ranks
Accepts: a list of integers number (list[int])

Input parameters#

Parameters#

bin_width#

peak_isolation#

max_output_configurations#

composition#

Examples#

which#

Examples#

structure#

Examples#

structure.supercell#

Examples#

mode#

iterations#

shell_distances#

shell_weights#

Examples#

pair_weights#

target_objective#

Examples#

prefactors#

prefactor_mode#

threads_per_rank#

`bin_width`#

`peak_isolation`#

`max_output_configurations`#

`composition`#

`which`#

`structure`#

`structure.supercell`#

`mode`#

`iterations`#

`shell_distances`#

`shell_weights`#

`pair_weights`#

`target_objective`#

`prefactors`#

`prefactor_mode`#

`threads_per_rank`#