First steps with PLAMS¶

Running the script¶

Create a working directory plams_tutorial, move into it, and save the script H2O_opti.py. To execute the script type in a terminal:

$ADFBIN/plams H2O_opti.py

The calculation should take just a few seconds and you should obtain the following result:

[12:06:21] PLAMS working folder: /home/username/plams_tutorial/plams_workdir
[12:06:21] JOB water_GO STARTED
[12:06:21] JOB water_GO RUNNING
[12:06:23] JOB water_GO FINISHED
[12:06:23] JOB water_GO SUCCESSFUL
== Water optimization Results ==
Bonding energy:  -323.40 kcal/mol
Bond angle:       103.64 degree
Optimized coordinates:
  Atoms:
    1         O      0.000000      0.000000     -0.067125
    2         H      0.000000     -0.771172     -0.673544
    3         H      0.000000      0.771172     -0.673544

[12:06:23] PLAMS run finished. Goodbye

Every time you run a PLAMS script, a new working directory is created (by default it is called plams_workdir). This folder will contain one subdirectory per job (in our case, one folder named water_GO). Each job folder contains the job’s input and results files.

Molecule¶

You can initialize a Molecule object by:

• creating an empty molecule and manually adding the atoms

  mol = Molecule()
  mol.add_atom(Atom(symbol='O', coords=(0,0,0)))
  mol.add_atom(Atom(symbol='H', coords=(1,0,0)))
  mol.add_atom(Atom(symbol='H', coords=(0,1,0)))
  

• importing the atomic coordinates from an external file (supported formats: xyz, mol, mol2, and pdb):

  mol = Molecule('molecules/H2O.xyz')
  

• using the rdkit interface to generate the grometry from a SMILES string:

  from scm.plams.interfaces.molecule.rdkit import from_smiles
  
  mol = from_smiles('O')
  

The Molecule class provides several methods for basic molecular manipulations, such as rotate, translate. Furthermore, the list of Atoms contained in the corresponding Molecule object can be directly accessed and manipulated. See the Molecule section of the PLAMS documentation for the complete list of methods.

Settings class¶

The Settings class extends the regular Python dictionary by many useful features. In the following we show how the Settings object is used to define the input for an ADFJob. The reader is strongly encouraged to consult the Settings section of the PLAMS documentation for a detailed explanation of the Settings class.

One important aspect of the PLAMS library is that PLAMS knows (almost) nothing about the input options of the various computational engines. In other words, you need to know the structure of the ASCII input file of the computational engine in order to set up the corresponding Settings object. Although input files for the various computational engines (ADF, BAND, and DFTB) use different sets of keywords, they share a similar structure – they consist of blocks and subblocks which in turn contain keys and values.

To give an example, the Settings object in H2O_opti.py

# Initialize the settings for the ADF calculation:
sett = Settings()
sett.input.basis.type = 'DZP'
sett.input.XC.GGA = 'PBE'
sett.input.geometry

will translate into the following ADF input file when supplied to an ADFJob class:

Basis
  Type DZP
End

Xc
  Gga PBE
End

Geometry
End

With the exception of the atomic coordinates (these will be picked up from the Molecule object; see above) all desired input options must be specified in the Settings object.

See the ADF Suite section of the PLAMS documentation for a comprehensive description of how the settings object is converted into an ASCII input file.

You can get the computational engine’s input file via the job’s get_input() method. For example:

job = ADFJob(molecule=mol, settings=sett, name='water_GO')
print(job.get_input())

Creating and running the Job¶

The Job class is the most important object in PLAMS as it takes care of the preparing, executing and collecting the results of a Job. For a detailed description of the Job class, see the Jobs section of the PLAMS documentation.

PLAMS ships several ready-to-use Job subclasses: ADFJob, AMSJob,... The base class Job can readily be extended to be interfaced with other computational engines.

Creating and running the ADFJob of our example is straightforward:

# Create and run the job:
job = ADFJob(molecule=mol, settings=sett, name='water_GO')
job.run()

Each job has its own subdirectory (with the same name as the job) in the working folder.

Results¶

After the successful completion of a job, the results can be accessed via the Results object associated with the job.

There are three ways of retrieving data from the job results:

• For the most common results like the final energy or optimized geometry there are dedicated methods like in the example above.

  energy = job.results.get_energy()
  opt_mol = job.results.get_main_molecule()
  

• For all other data the recommended approach is to read the binary results file (in KF format) by using readkf. The readkf method will access the appropriate binary file (TAPE21 for ADF) and returns the requested data in a python built-in type (int, float, bool, ...). If the requested data is an array, it returns a python list.

  # Fetch and print some results:
  energy = job.results.readkf('Energy', 'Bond Energy')
  

  The two arguments are the respective “Section” and “Variable” names in the binary KF result file.

  Tip

  You can check the content of a binary KF file with the kfbrowser tool

  Most results in the binary KF file are provided in atomic units. The Units utility can be used to convert the results into the desired units:

  energy_in_kcal_mol = Units.convert(energy_in_au, 'au', 'kcal/mol')
  

• You can also extract data from the standard output using the grep_output and awk_output methods. They will return a list of strings containing the matches of the grep or awk commands. Alternatively, you can use get_output_chunk to extract a continuous part of the output between two lines.

You can get the optimized geometry into a Molecule object via the get_main_molecule method:

opt_mol = job.results.get_main_molecule()