Running a PWscf calculation¶

This section will show how to launch a single PWscf (pw.x executable) calculation. It is assumed that you have already performed the installation, and that you already:

• setup a computer (with verdi);
• installed Quantum ESPRESSO on your machine or a cluster;
• setup the code and computer you want to use.

Although the code could be quite readable, a basic knowledge of Python and object programming is useful.

The classic pw.x input file¶

This is the input file of Quantum ESPRESSO that we will try to execute. It consists in the total energy calculation of a 5 atom cubic cell of BaTiO3. Note also that AiiDA is a tool to use other codes: if the following input is not clear to you, please refer to the Quantum ESPRESSO documentation.

&CONTROL
calculation = 'scf'
outdir = './out/'
prefix = 'aiida'
pseudo_dir = './pseudo/'
restart_mode = 'from_scratch'
verbosity = 'high'
wf_collect = .true.
/
&SYSTEM
ecutrho =   2.4000000000d+02
ecutwfc =   3.0000000000d+01
ibrav = 0
nat = 5
ntyp = 3
/
&ELECTRONS
conv_thr =   1.0000000000d-06
/
ATOMIC_SPECIES
Ba     137.33    Ba.pbesol-spn-rrkjus_psl.0.2.3-tot-pslib030.UPF
Ti     47.88     Ti.pbesol-spn-rrkjus_psl.0.2.3-tot-pslib030.UPF
O      15.9994   O.pbesol-n-rrkjus_psl.0.1-tested-pslib030.UPF
ATOMIC_POSITIONS angstrom
Ba           0.0000000000       0.0000000000       0.0000000000
Ti           2.0000000000       2.0000000000       2.0000000000
O            2.0000000000       2.0000000000       0.0000000000
O            2.0000000000       0.0000000000       2.0000000000
O            0.0000000000       2.0000000000       2.0000000000
K_POINTS automatic
4 4 4 0 0 0
CELL_PARAMETERS angstrom
4.0000000000       0.0000000000       0.0000000000
0.0000000000       4.0000000000       0.0000000000
0.0000000000       0.0000000000       4.0000000000


Without AiiDA, not only you would have to prepare ‘manually’ this file, but also prepare the scheduler submission script, send everything on the cluster, etc. We are going instead to prepare everything in a more programmatic way.

Running a pw calculation with AiiDA¶

Now we are going to prepare a script to submit a job to your local installation of AiiDA. This example will be a rather long script: in fact it assumes that nothing in your database, so that we will have to load everything, like the pseudopotential files and the structure. In a more practical situation, you might load data from the database and perform a small modification to re-use it.

Let’s say that through the verdi command you have already installed a cluster, say TheHive, and that you also compiled Quantum ESPRESSO on the cluster, and installed the code pw.x with verdi with label pw-6.3 for instance, so that in the rest of this tutorial we will reference to the code as pw-6.3@TheHive.

Let’s start writing the python script. First of all, we need to load the configuration concerning your particular installation, in particular, the details of your database installation:

#!/usr/bin/env python


Code¶

Now we have to select the code. Note that in AiiDA the object ‘code’ in the database is meant to represent a specific executable, i.e. a given compiled version of a code. Every calculation in AiiDA is linked to a code, installed on a specific computer. This means that if you install Quantum ESPRESSO on two computers A and B, you will need to have two different ‘codes’ in the database (although the source of the code is the same, the binary file is different).

If you setup the code pw-6.3 on machine TheHive correctly, then it is sufficient to write:

codename = 'pw-6.3@TheHive'
from aiida.orm import Code
code = Code.get_from_string(codename)


where in the last line we just load the database object representing the code.

Note

the .get_from_string() method is just a helper method for user convenience, but there are some weird cases that cannot be dealt in a simple way (duplicated labels, code names that are an integer number, code names containing the ‘@’ symbol, …): try to not do this! This is not an error, but does not allow to use the .get_from_string() method to get those calculations. You can use directly the .get() method, for instance:

code = Code.get(label='pw-6.3', machinename='TheHive')


or even more generally get the code from its (integer) PK:

code = load_node(PK)


Once you have a code, you can start to assemble the inputs to run a PWscf calculation.

Note

To learn more about calculations and processes in AiiDA you can refer to the working_calculations and concepts_processes sections of the AiiDA manual Remember that what is shown here refers to the Quantum ESPRESSO plugin: different codes will in general required different inputs.

Preparing a calculation¶

To make it easier to prepare the inputs of an AiiDA calculation, process classes have a get_builder method, that simplify the access to the inputs of a calculation.

To use it, you can load the calculation class that you need through the CalculationFactory

PwCalculation = CalculationFactory('quantumespresso.pw')
builder = PwCalculation.get_builder()


In a similar way, you can use the get_builder utility from a code, with the advantage that the code input gets automatically populated:

builder = code.get_builder()


If you are using the builder to a verdi shell, you will see all the inputs that are available for a calculation by pressing the TAB key after typing builder.. To understand what type of data is expected for a particular input, you can append the ? or the ?? at the end of an input. For example:

>>> builder.structure?
Type:        property
String form: <property object at 0x7f58bdcc6728>
Docstring:   {"name": "structure", "required": "True", "valid_type": "<class 'aiida.orm.nodes.data.structure.StructureData'>", "help": "The input structure."}


In this case, the helper lets you know you what kind of data it expects (StructureData), that it is a required input for the calculation, and a short string explaining what the structure input represents.

The plugin requires at least the presence of:

• An input structure;
• A k-points mesh, in the form of a KpointsData object;
• Pseudopotential files for the atomic species present;
• A parameters dictionary, that contains the details of the Quantum ESPRESSO calculation;

Other inputs are optional, for example:

• metadata is a dictionary of inputs that modify slightly the behaviour of a processes;
• settings is a Dict dictionary that provides access to more advanced, non-default feature of the code.

Structure¶

We now proceed in setting up the structure.

Note

Here we discuss only the main features of structures in AiiDA, needed to run a Quantum ESPRESSO PW calculation.

For more detailed information, give a look to the structure_tutorial.

There are two ways to do that in AiiDA, a first one is to use the AiiDA Structure, which we will explain in the following; the second choice is the Atomic Simulation Environment (ASE) which provides excellent tools to manipulate structures (the ASE Atoms object needs to be converted into an AiiDA Structure, see the note at the end of the section).

We first have to load the abstract object class that describes a structure. We do it in the following way: we load the DataFactory, which is a tool to load the classes by their name, and then call StructureData the abstract class that we loaded. Note that it is not yet a class instance! If you are not familiar with the terminology of object programming, you can look at Wikipedia for their short explanation: in common speech that one refers to a file as a class, while the file is the object or the class instance. In other words, the class is our definition of the object Structure, while its instance is what will be saved as an object in the database:

from aiida.plugins import DataFactory
StructureData = DataFactory('structure')


We define the cell with a 3x3 matrix (we choose the convention where each ROW represents a lattice vector), which in this case is just a cube of size 4 Angstroms:

alat = 4. # angstrom
cell = [[alat, 0., 0.,],
[0., alat, 0.,],
[0., 0., alat,],
]


Now, we create the StructureData instance, assigning immediately the cell. Then, we append to the empty crystal cell the atoms, specifying their element name and their positions:

# BaTiO3 cubic structure
s = StructureData(cell=cell)
s.append_atom(position=(0.,0.,0.),symbols='Ba')
s.append_atom(position=(alat/2.,alat/2.,alat/2.),symbols='Ti')
s.append_atom(position=(alat/2.,alat/2.,0.),symbols='O')
s.append_atom(position=(alat/2.,0.,alat/2.),symbols='O')
s.append_atom(position=(0.,alat/2.,alat/2.),symbols='O')


To see more methods associated to the StructureData class, look at the my-ref-to-structure documentation on the AiiDA manual.

Note

When you create a node (in this case a StructureData node) as described above, you are just creating it in the computer memory, and not in the database. This is particularly useful to run tests without filling the AiiDA database with garbage.

It is not necessary to store anything to the database at this point; if, however, you want to directly store the structure in the database for later use, you can just call the store() method of the Node:

s.store()


For an extended tutorial about the creation of Structure objects, check this tutorial on the AiiDA-core documentation.

Note

AiiDA also supports ASE structures. Once you created your structure with ASE, in an object instance called say ase_s, you can straightforwardly use it to create the AiiDA StructureData, as:

s = StructureData(ase=ase_s)


and then save it s.store().

Parameters¶

Now we need to provide also the parameters of a Quantum ESPRESSO calculation, like the cutoff for the wavefunctions, some convergence threshold, and so on. The Quantum ESPRESSO pw.x plugin requires to pass this information within a Dict object, that is a specific AiiDA data node that can store a dictionary (even nested) of basic data types: integers, floats, strings, lists, dates, … We first load the class through the DataFactory, just like we did for the Structure. Then we create the instance of the object parameter. To represent closely the structure of the Quantum ESPRESSO input file, Dict is a nested dictionary, at the first level the namelists (capitalized), and then the variables with their values (in lower case).

Note also that numbers and booleans are written in Python, i.e. False and not the Fortran string .false.!

Dict = DataFactory('dict')

parameters = Dict(dict={
'CONTROL': {
'calculation': 'scf',
'restart_mode': 'from_scratch',
'wf_collect': True,
},
'SYSTEM': {
'ecutwfc': 30.,
'ecutrho': 240.,
},
'ELECTRONS': {
'conv_thr': 1.e-6,
}
})


Note

also in this case, we chose not to store the parameters node. If we wanted, we could even have done it in a single line:

parameters = Dict(dict={...}).store()


The experienced Quantum ESPRESSO user will have noticed also that a couple of variables are missing: the prefix, the pseudo directory and the scratch directory are reserved to the plugin, which will use default values, and there are specific AiiDA methods to restart from a previous calculation.

Input parameters validation¶

The dictionary provided above is the standard format for storing the inputs of Quantum ESPRESSO pw.x in the database. It is important to store the inputs of different calculations in a consistent way because otherwise later querying becomes impossible (e.g. if different units are used for the same flags, if the same input is provided in different formats, and so on).

In the PW input plugin, we provide a function that will help you in both validating the input, and creating the input in the expected format without remembering in which namelists the keywords are located.

You can access this function as follows. First, you define the input dictionary:

test_dict = {
'CONTROL': {
'calculation': 'scf',
},
'SYSTEM': {
'ecutwfc': 30.,
},
'ELECTRONS': {
'conv_thr': 1.e-6,
}
}


Then, you can verify if the input is correct by using the pw_input_helper() function, conveniently exposes also as a input_helper class method of the PwCalculation class:

resdict = CalculationFactory('quantumespresso.pw').input_helper(test_dict, structure=s)


If the input is invalid, the function will raise a InputValidationError exception, and the error message will have a verbose explanation of the possible errors, and in many cases it will suggest how to fix them. Otherwise, in resdict you will find the same dictionary you passed in input, potentially slightly modified to fix some small mistakes (e.g., if you pass an integer value where a float is expected, the type will be converted). You can then use the output for the input Dict node:

parameters = Dict(dict=resdict)


As an example, if you pass an incorrect input, e.g. the following where we have introduced a few errors:

test_dict = {
'CONTROL': {
'calculation': 'scf',
},
'SYSTEM': {
'ecutwfc': 30.,
'cosab': 10.,
'nosym': 1,
},
'ELECTRONS': {
'convthr': 1.e-6,
'ecutrho': 100.
}
}


After running the input_helper method, you will get an exception with a message similar to the following:

QEInputValidationError: Errors! 4 issues found:
* You should not provide explicitly keyword 'cosab'.
* Problem parsing keyword convthr. Maybe you wanted to specify one of these: conv_thr, nconstr, forc_conv_thr?
* Expected a boolean for keyword nosym, found <type 'int'> instead
* Error, keyword 'ecutrho' specified in namelist 'ELECTRONS', but it should be instead in 'SYSTEM'


As you see, a quite large number of checks are done, and if a name is not provided, a list of similar valid names is provided (e.g. for the wrong keyword “convthr” above).

There are a few additional options that are useful:

• If you don’t remember the namelists, you can pass a ‘flat’ dictionary, without namelists, and add the flat_mode=True option to input_helper. Beside the usual validation, the function will reconstruct the correct dictionary to pass as input for the AiiDA Quantum ESPRESSO calculation. Example:

test_dict_flat = {
'calculation': 'scf',
'ecutwfc': 30.,
'conv_thr': 1.e-6,
}
resdict = CalculationFactory('quantumespresso.pw').input_helper(
test_dict_flat, structure=s, flat_mode = True)


and after running, resdict will contain:

test_dict = {
'CONTROL': {
'calculation': 'scf',
},
'SYSTEM': {
'ecutwfc': 30.,
},
'ELECTRONS': {
'conv_thr': 1.e-6,
}
}


where the namelists have been automatically generated.

• You can pass a string with a specific version number for a feature that was added only in a given version. For instance:

resdict = CalculationFactory('quantumespresso.pw').input_helper(
test_dict, structure=s,version='5.3.0')


If the specific version is not among those for which we have a list of valid parameters, the error message will tell you which versions are available.

Note

We will try to maintain the input_helper every time a new version of Quantum ESPRESSO is released, but consider the input_helper function as a utility, rather than the official way to provide the input – the only officially supported way to provide an input to pw.x is through a direct dictionary, as described earlier in the section “Parameters”. This applies in particular if you are using very old versions of Quantum ESPRESSO, or customized versions that accept different parameters.

Multi-dimensional variables¶

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 Hubbard_U(i) keyword of the SYSTEM card. The Hubbard U value needs to be applied to a specific species and therefore the index i is required to be able to make this distinction. Note that the value of i needs to correspond to the index of the species to which the Hubbard U value needs to be applied.

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 Hubbard U 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 what it to have a Hubbard U value, one can add the following in the parameter data dictionary:

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


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

&SYSTEM
hubbard_u(1) = 4.5
/
ATOMIC_SPECIES
Co     58.933195 Co_pbe_v1.2.uspp.F.UPF
Li     6.941 li_pbe_v1.4.uspp.F.UPF
O      15.9994 O_pbe_v1.2.uspp.F.UPF


Note that since Co is listed as the first atomic species, the index in the hubbard_u(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:

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)


However, 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. The list of keywords that match this description:

efield_cart(i)
fixed_magnetization(i)
hubbard_j(i,ityp)
starting_ns_eigenvalue(m,ispin,I)


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) = 3.5
hubbard_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.

Note

Nota bene: The code will not verify that a keyword actually requires an atomic species index in a certain position, and will indiscriminately map the value to an atomic species index if that value corresponds to a kind name.

K-points mesh¶

The k-points have to be saved in another kind of data, namely KpointsData:

KpointsData = DataFactory('array.kpoints')
kpoints = KpointsData()
kpoints.set_kpoints_mesh([4,4,4])


In this case it generates a 4*4*4 mesh without offset. To add an offset one can replace the last line by:

kpoints.set_kpoints_mesh([4,4,4],offset=(0.5,0.5,0.5))


Note

Only offsets of 0 or 0.5 are possible (this is imposed by PWscf).

You can also specify kpoints manually, by inputing a list of points in crystal coordinates (here they all have equal weights):

import numpy
kpoints.set_kpoints([[i,i,0] for i in numpy.linspace(0,1,10)],
weights = [1. for i in range(10)])


A Gamma point calculation can be submitted by providing the ‘gamma_only’ flag to the options dictionary

kpoints.set_kpoints_mesh([1,1,1])
builder.settings = Dict(dict={'gamma_only': True})


Pseudopotentials¶

There is still one missing piece of information, that is the pseudopotential files, one for each element of the structure.

Note

For a more extended documentation on how to import pseudopotentials in the database, and how to handle and instal pseudopotential families, you can find more information in my-ref-to-pseudo-tutorial.

The builder.pseudos input is a dictionary, where the keys are the names of the elements, and the values are the UpfData objects stored in the database.

It is possible to specify manually which pseudopotential files to use for each atomic species. However, for any practical use, it is convenient to use the pseudopotential families.

If you got one installed, you can simply tell the calculation to use the pseudopotential family with a given name, and AiiDA will take care of linking the proper pseudopotentials to the calculation, one for each atomic species present in the input structure. This can be done using:

from aiida.orm.nodes.data.upf import get_pseudos_from_structure
builder.pseudos = get_pseudos_from_structure(structure, <PSEUDOPOTENTIAL_FAMILY_NAME>)


Note

The list of pseudopotential families installed in your database can be accessed by command line with

verdi data upf listfamilies


Sometimes it is useful to attach some notes to the calculation, that may help you later understand why you did such a calculation, or note down what you understood out of it. Comments are a special set of properties of the calculation, in the sense that it is one of the few properties that can be changed, even after the calculation has run.

These properties can be set in the metadata input of the calculation, with a label (a short description) and description (longer)

builder.metadata.label = 'My generic title'
builder.metadata.description ' a PWscf calculation of BaTiO3'


Note

The TAB expansion works also on nested properties: from builder.metadata. you can explore the available options

Calculation resources¶

General options that are independent on the code or the plugin are grouped under builder.metadata.options. Here you can set up the resource that you want to allocate to this calculation, that will be passed to the scheduler that handles the queue on your computer:

builder.metadata.options.resources = {'num_machines': 1}


More options are available, and can be explored by expanding builder.metadata.options. + TAB.

Launching the calculation¶

If we are satisfied with what you created, it is time to attach all the required inputs to the calculation:

builder.structure = structure
builder.kpoints = kpoints
builder.parameters = parameters


The data nodes do not need to be stored at this point, as AiiDA will store them upon submission.

To execute the calculation, there are two possible ways:

• run: the calculation gets executed in the shell, locking it until it is finished;
• submit: the calculation is handled to the AiiDA daemon, and it will be running in the background

The commands are explained more in detail in the working_processes_launching documentation of AiiDA.

To run your calculation, you can execute:

from aiida.engine import run
results = run(builder)


where the results variable will contain a dictionary containing all the nodes that were produced as output. Alternatively, it is possible to use either run.get_node or run.get_pk methods to retrive more information about the calculation node:

from aiida.engine import run
result, node = run.get_node(builder)
result, pk = run.get_pk(builder)


where pk and node will contain, respectively, the node object of the calculation or its pk.

To submit the calculation to the daemon, you can use instead

from aiida.engine import submit
calc = submit(builder)


Note that, in this case, calc is the calculation node, and not the result dictionary.

Note

In order to inspect the inputs created by AiiDA without actually running the calculation, we can perform a dry run of the submission process:

builder.metadata.dry_run = True


This will create the input files, that are available for inspection.

Note

You’re not forced to assign the inputs through the builder: they can be provided as keywords argument when you launch the calculation, passing the calculation class as the first argument:

run(PwCalculation, structure=s, pseudos=pseudos, kpoints = kpoints, ...)


The calculation results are directly accessible as a result of the run job, or they can be easily accessed from calc.res., a shortcut to the calc.outputs.output_parameters dictionary:

calc.res.energy
calc.res.energy_units


to access the final energy and its units.

Summarizing, we created all the inputs needed by a PW calculation, that are: parameters, kpoints, pseudopotential files and the structure. We then created the calculation, where we specified that it is a PW calculation and we specified the details of the remote cluster.

To continue the tutorial with the ph.x phonon code of Quantum ESPRESSO, continue here: Phonon.

Script: source code¶

In this section you’ll find two scripts that do what explained in the tutorial. The compact is a script with a minimal configuration required. You can copy and paste it (or download it), modify the two strings codename and pseudo_family with the correct values, and execute it with:

python pw_short_example.py


(It requires to have one family of pseudopotentials configured).

Download: this example script

Importing previously run Quantum ESPRESSO pw.x calculations: PwImmigrant¶
This can be achieved with the PwImmigrant class described below, for which you can find a tutorial here.