pw.x — aiida-quantumespresso documentation (2024)

Multidimensional parameters#

The input format of pw.x contains various keywords that do not simply take the format of a key value pair, but rather there will some indices in the key itself.Take for example the starting_magnetization keyword of the SYSTEM card.The starting magnetization value needs to be applied to a specific species and therefore the index i is required to be able to make this distinction.

The PwCalculation plugin makes this easy as it will do the conversion from kind name to species index automatically.This allows you to specify a starting magnetization value by using a dictionary notation, where the key is the kind name to which it should be applied.For example, if you have a structure with the kind Co and want it to have a given starting magnetization, one can add the following in the parameter data dictionary:

parameters = { 'SYSTEM': { 'starting_magnetization': { 'Co': 4.5 } }}

This part of the parameters dictionary will be transformed by the plugin into the following input file:

&SYSTEM starting_magnetization(1) = 4.5/ATOMIC_SPECIESCo 58.93 Co.UPFLi 6.941 Li.UPFO 15.99 O.UPF

Note that since Co is listed as the first atomic species, the index in the starting_magnetization(1) keyword reflects this.The usage of a dictionary where the keys correspond to a kind of the input structure, will work for any keyword where the index should correspond to the index of the atomic species.Examples of keywords where this approach will work are:

• angle1(i)

• angle2(i)

• hubbard_alpha(i)

• hubbard_beta(i)

• hubbard_j0(i)

• hubbard_u(i)

• london_c6(i)

• london_rvdw(i)

• starting_charge(i)

• starting_magnetization(i)

There are also keywords that require more than index, or where the single index actually does not correspond to the index of an atomic species, such as the starting_ns_eigenvalue parameters.To allow one to define these keywords, one can use nested lists, where the first few elements constitute all the index values and the final element corresponds to the actual value.For example the following:

parameters = { 'SYSTEM': { 'starting_ns_eigenvalue': [ [1, 1, 3, 3.5], [2, 1, 1, 2.8] ] }}

will result in the following input file:

&SYSTEM starting_ns_eigenvalue(1,1,3) = 3.5 starting_ns_eigenvalue(2,1,1) = 2.8/

Note that any of the values within the lists that correspond to a kind in the input structure, will be replaced with the index of the corresponding atomic species.For example:

hubbard_j: [ [2, 'Ni', 3.5], [2, 'Fe', 7.4],]

would be formatted as:

hubbard_j(2, 1) = 3.5hubbard_j(2, 3) = 7.4

Assuming the input structure contained the kinds Ni and Fe, which would have received the atomic species indices 1 and 3 in the ultimate input file, respectively.

Getting pseudopotentials from a family#

If pseudopotentials have been installed as a family using the aiida-pseudo package, they can be retrieved as follows:

from ase.build import bulkfrom aiida.orm import StructureData, load_code, load_group# Load the pseudopotential family whose pseudos to usefamily = load_group('SSSP/1.3/PBEsol/effiency')structure = StructureData(ase=bulk('GaAs', 'fcc', 5.4))builder = load_code('pw').get_builder()builder.pseudos = family.get_pseudos_from_structure(structure=structure)

The get_pseudos() method will automatically return a dictionary with the pseudos necessary for the specified structure that can be immediately assigned to the pseudos input of the builder.

Getting recommended cutoffs from a family#

Certain pseudopotential families provided by aiida-pseudo, such as the SsspFamily, provide recommended energy cutoffs for the wave function and charge density, for each pseudopotential they provide.Similar to the pseudopotentials themselves, these can easily be retrieved for any given StructureData:

from ase.build import bulkfrom aiida.orm import StructureData, load_code, load_group# Load the pseudopotential family whose pseudos to usefamily = load_group('SSSP/1.3/PBEsol/effiency')structure = StructureData(ase=bulk('GaAs', 'fcc', 5.4))builder = load_code('pw').get_builder()cutoff_wfc, cutoff_rho = family.get_recommended_cutoffs(structure=structure, unit='Ry')builder.parameters = { 'SYSTEM': { 'ecutwfc': cutoff_wfc, 'ecutrho': cutoff_rho, }}

Be sure to specify the unit as Ry as that is the unit that pw.x will expect.

How to check the SCF accuracy during the self-consistent cycle#

During the self-consistent field cycle, the difference in energy of the newly computed charge density and the starting one is computed and stored.It can easily be retrieved through the get_scf_accuracy() method.This method can be accessed directly through the tools of a completed PwCalculation node:

In [1]: node = load_node(IDENTIFIER)In [2]: node.tools.get_scf_accuracy()Out[2]:array([1.22958881e+00, 7.84853851e-02, 5.10948147e-03, 5.42404506e-03, 2.94427169e-04, 9.25187037e-06])

This returns a numpy array with the SCF accuracy at each step during the complete cycle.If the calculation contained dynamics, i.e. the atomic positions were updated such as in a relax or vc-relax calculation, there will be multiple SCF cycles, one for each frame of the dynamics trajectory.To get the SCF accuracy progression of a particular frame, specify it using the index argument:

In [1]: node = load_node(IDENTIFIER)In [2]: node.tools.get_scf_accuracy(index=-1)Out[2]:array([1.38253747e+00, 5.99484465e-02, 1.20151864e-03, 4.69396365e-05, 4.08170752e-06])

The index supports negative values to start counting from the back, just as with regular numpy arrays.