7.1.1. ResultsImporter overview¶
7.1.1.1. ResultsImporter summary¶
The ResultsImporter
class is convenient for setting up the Job Collection, Data Set, and Engine Collection from
finished reference calculations. A results importer typically affects all three of those classes
with a single method (see Import training data (Python))
Most ResultsImporters work on finished reference jobs, coming from one of these computational chemistry programs:
Program |
Required results files |
---|---|
AMS (ADF, BAND, DFTB, ReaxFF, …) |
|
VASP |
|
Quantum ESPRESSO |
Standard output file (ending in |
Gaussian |
Standard output file (ending in |
The ResultsImporter
reads the input and output from the reference job,
creates a reference engine in the Engine Collection if it does not already exist,
adds one or more jobs to the Job Collection using one or more systems from the reference job, if they do not already exist
adds one or more entries to the Data Set
The Import training data (Python) gives an example on how to use ResultsImporters.
All ResultsImporters are summarized in the below table, where SP stands for SinglePoint, GO for GeometryOptimization, MD for MolecularDynamics, and QE for Quantum ESPRESSO.
ResultsImporter name |
||||||
---|---|---|---|---|---|---|
Reference job type |
any |
GO or MD |
SP, GO, or None 1 |
PESScan |
NEB |
PESExploration |
AMS/VASP/QE |
AMS/VASP/QE |
AMS/VASP/QE |
AMS |
AMS |
AMS |
|
# jobs added |
1 |
any |
1 per species |
any |
any |
any |
Default task |
SP |
SP |
SP |
SP |
SP |
SP |
Allowed tasks |
any |
SP |
SP or GO |
SP |
SP |
SP |
Systems |
final frame 2 |
any |
final frames |
converged points |
converged points |
minima and transition states |
# data_set entries |
any |
any |
1 |
any |
any |
|
Properties |
all extractors |
traj. prop. 3 |
None |
energy/relative_energies/stresstensor* |
energy/relative_energies |
energy/relative_energies |
API docs |
7.1.1.2. ResultsImporter settings¶
When initializing a ResultsImporter
, you can pass a settings
argument, that is
either a string pointing to a results_importer_settings.yaml
file, or which is a
PLAMS Settings instance.
Example:
results_importer_settings = Settings()
results_importer_settings.trim_settings = True
results_importer_settings.default_go_settings.MaxIterations = 30
results_importer_settings.units.energy = 'kcal/mol'
ri = ResultsImporter(settings=results_importer_settings)
For a detailed description of the available settings, see __init__()
.
7.1.1.3. Training set, validation set, etc.¶
See the tutorial: Training set, validation set, and other data sets.
7.1.1.4. Save and load from disk¶
The job collection, engine collection, data_set(s), and shortcut settings can be
stored for later retrieval with the store()
method.
To load them from files, pass the paths to the files as arguments to
__init__()
.
For an example, see the tutorial: Save to disk.
7.1.1.5. add_singlejob¶
add_singlejob()
adds 1 job to the job collection, together with an arbitrary number of entries in the data_set.
For how to use it, see the Import training data (Python), in particular
See also the API docs: add_singlejob()
Each entry in the data_set must be an extractor acting on the job.
Adding for example 'distance(0,1)'
to the properties
argument will add a data_set entry with the expression distance('jobname',0,1)
.
Example:
ri = ResultsImporter()
ri.add_singlejob('/path/to/ams.rkf',
properties=['energy', 'distance(0,1)'],
task='GeometryOptimization')
7.1.1.6. add_trajectory_singlepoints¶
add_trajectory_singlepoints()
extracts given number of frames from a trajectory file.
The properties
must be one or more of the following: energy
,
relative_energies
, forces
, or stresstensor
.
For how to use it, see Import training data (Python), in particular
See also the API docs: add_trajectory_singlepoints()
Example:
ri = ResultsImporter()
# extract every 10 frames
ri.add_trajectory_singlepoints('/path/to/ams.rkf',
properties=['forces', 'relative_energies'],
start=0,
step=10)
# extract 20 frames
ri.add_trajectory_singlepoints('/path/to/ams.rkf',
properties=['forces', 'relative_energies'],
N=20)
7.1.1.7. add_reaction_energy¶
add_reaction_energy()
calculates and adds a reaction energy to the
data_set. The reaction is automatically balanced: only the reactants and
products need to specified, not the stoichiometric coefficients.
The coefficients are normalized according to the normalization
and normalization_value
arguments.
Setting normalization='r0'
will set the coefficient for the first reactant
to normalization_value
. normalization='r1'
would correspond to the
second reactant, etc. normalization='p0'
would correspond to the first
product, etc.
add_reaction_energy can also be used with PLAMS molecules instead of reference
jobs. In that case one should specify the reference value with the
reference
keyword.
For how to use it, see Import training data (Python), in particular
See also the API docs: add_reaction_energy()
Simple example:
ri = ResultsImporter()
# from reference jobs
ri.add_reaction_energy(reactants=['/reactant1/ams.rkf', '/reactant2/ams.rkf'],
products=['/product1/ams.rkf', '/product2/ams.rkf'],
normalization='r0',
normalization_value=1.0)
# from Molecule instances. mol_1 etc. need to be PLAMS Molecules
ri.add_reaction_energy(reactants=[mol_1, mol_2],
products=[mol_3, mol_4],
normalization='p1',
normalization_value=2.0,
reference=12.34,
unit='kcal/mol')
7.1.1.8. add_pesscan_singlepoints¶
add_pesscan_singlepoints()
extracts a given number of (converged) frames from an AMS PESScan reference job, and adds them as single point calculations to the job collection.
Important
If you want to add a job with Task PESScan
to the job collection, do not
use this shortcut but instead add_singlejob.
Tip
Adding singlepoint calculations from a PESScan with
add_pesscan_singlepoints(job, properties='relative_energies')
will give
a much faster parametrization than using add_singlejob(job,
properties='pes', task='PESScan')
. However, we in general recommend to use
add_singlejob
instead, since it will give a more accurate
parametrization of the potential energy surface.
The properties
must be one or more of the following: energy
, relative_energies
, stresstensor*
(stresstensor
, stresstensor_2d
, etc., see Available Extractors).
Note
Extracting the stress tensor from an energy-volume scan will only work if
CalcPropertiesAtPESPoints was set when running the job, i.e. if the files
PESPoint(1).rkf
etc. exist in the same directory as ams.rkf
.
See the API docs for details: add_pesscan_singlepoints()
Example:
ri = ResultsImporter()
ri.add_pesscan_singlepoints('/path/to/ams.rkf', # ams.rkf from a PESScan job
properties=['relative_energies'])
7.1.1.9. add_neb_singlepoints¶
add_neb_singlepoints()
extracts the converged frames from an AMS NEB reference job, and adds them as single point calculations to the job collection.
Important
If you want to add a job with Task NEB
to the job collection, do not use this shortcut but instead add_singlejob.
The properties
must be one or more of the following: energy
, relative_energies
.
See the API docs for details: add_neb_singlepoints()
Example:
ri = ResultsImporter()
ri.add_neb_singlepoints('/path/to/ams.rkf', # ams.rkf from a NEB job
properties=['relative_energies'])
7.1.1.10. add_pesexploration_singlepoints¶
add_pesexploration_singlepoints()
extracts a given list of states an AMS
PESExploration reference job, and adds them as single point calculations to the
job collection.
Important
If you want to add a job with Task PESExploration
to the job collection
(although you probably do not because of how computationally demanding such
jobs are), do not use this shortcut but instead add_singlejob.
The properties
must be one or more of the following: energy
, relative_energies
.
See the API docs for details: add_pesexploration_singlepoints()
Example:
ri = ResultsImporter()
ri.add_pesexploration_singlepoints('/path/to/ams.rkf', # ams.rkf from a PESExploration job
properties=['relative_energies'],
indices=[1,2,5,6]) # state numbers (start with 1)