Welcome to pnictogen

pnictogen is both a library and a command-line tool that helps composing input files for computational chemistry packages. It is based on Jinja2, a modern and friendly templating language for Python:

$ cat new_template.ORCA.inp
# {{ molecule.name }}
! Opt

* xyz {{ molecule.charge }} {{ molecule.mult }}
{{ molecule.to_string("xyz") }}
*
$ pnictogen new_template.ORCA.inp data/water.xyz
data/water.inp written
$ cat data/water.inp
# A water molecule
! Opt

* xyz 0 1
O          0.05840        0.05840        0.00000
H          1.00961       -0.06802        0.00000
H         -0.06802        1.00961        0.00000
*

Installation

You can get pnictogen directly from PyPI:

$ pip install -U pnictogen

The above will install the the version from PyPI, which is recommended.

For the development version, clone this repository and run:

$ pip install -U -e .

Tutorial

pnictogen can currently create boilerplates for ADF, DALTON, GAMESS (US), GAMESS-UK, Gaussian, Jaguar, Molpro, MOPAC, MPQC, NWChem, ORCA, Psi, Q-Chem and ZINDO:

$ pnictogen -g new_template.MOPAC.mop
new_template.MOPAC.mop written
$ cat new_template.MOPAC.mop
CHARGE={{ molecule.charge }} MS={{ (molecule.mult - 1)/2 }}
{{ molecule.name }}

{{ molecule.to_string("mop") }}

(pnictogen -g new_template.inp creates a blank file.)

You can either create and edit a boilerplate template or start fresh. Once you have a template, generating inputs is easy:

$ pnictogen new_template.ORCA.inp data/co.xyz data/water.xyz
data/co.inp written
data/water.inp written

(Wildcards are allowed, e.g., pnictogen new_template.ORCA.inp *.xyz works.)

Since pnictogen is built on top of Pybel, so it is able to read anything Open Babel reads. Check the list of all available file formats here.

Templates

You can use the full Jinja2 syntax within templates (check here its documentation for details).

Besides this, pnictogen also understands a special delimiter (--@) that allows one to generate many inputs from a single file:

$ cat repo/MOPAC.mop
{% for molecule in molecule %}
--@{{ loop.index }}
CHARGE={{ molecule.charge }} MS={{ (molecule.mult - 1)/2 }}
{{ molecule.name }}

{{ molecule.to_string("mop") }}

{% endfor %}
$ pnictogen repo/MOPAC.mop data/pentane_conformers.xyz
data/pentane_conformers_1.mop written
data/pentane_conformers_2.mop written
data/pentane_conformers_3.mop written
data/pentane_conformers_4.mop written
data/pentane_conformers_5.mop written
data/pentane_conformers_6.mop written
data/pentane_conformers_7.mop written

The rest of the line after --@ is aways added to the name of the inputs after an underscore (_).

In the example above, data/pentane_conformers.xyz contains seven conformers of pentane, so seven inputs were generated (the counting is provided by loop.index):

$ cat data/pentane_conformers_5.mop
CHARGE=0 MS=0.0
C5H12

C   1.23923 1  1.46892 1 -1.23930 1
C   1.24920 1  0.57161 1  0.00000 1
C  -0.00000 1 -0.31179 1 -0.00000 1
C  -1.24920 1  0.57161 1 -0.00000 1
C  -2.49842 1 -0.31168 1  0.01981 1
H   1.23217 1  0.84960 1 -2.13625 1
H   0.34926 1  2.09811 1 -1.22516 1
H   2.12917 1  2.09831 1 -1.23936 1
H   2.13917 1 -0.05758 1 -0.01415 1
H   1.25625 1  1.19094 1  0.89694 1
H  -0.00000 1 -0.94109 1 -0.89000 1
H  -0.00000 1 -0.94109 1  0.89000 1
H  -1.24217 1  1.21085 1  0.88286 1
H  -1.25629 1  1.19089 1 -0.89697 1
H  -2.50545 1 -0.95092 1 -0.86305 1
H  -2.49134 1 -0.93096 1  0.91678 1
H  -3.38842 1  0.31762 1  0.01981 1

Example: energy decomposition analysis (EDA) with ADF

Imagine we want to do energy decomposition analysis on the following water dimer:

$ cat water-dimer.xyz
6

O          0.12908       -0.26336        0.64798
H          0.89795        0.28805        0.85518
H          0.10833       -0.20468       -0.33302
O          0.31020        0.07569       -2.07524
H          0.64083       -0.57862       -2.71449
H         -0.26065        0.64232       -2.62218

The following template uses both molecule.split() and molecule.to_string("xyz") functions to generate ADF inputs in bulk:

$ cat split.ADF.in
{% set frags = molecule.split([range(3), range(3, 6)]) %}
--@eda
ATOMS Cartesian
{% for frag in frags %}
{{ frag.to_string("xyz", dialect="adf", fragment_id="f{}".format(loop.index)) }}
{% endfor %}
End

Fragments
{% for frag in frags %}
 f{{ loop.index }} {{ input_prefix }}_f{{ loop.index }}.t21
{% endfor %}
End

{% for frag in frags %}
--@f{{ loop.index }}
ATOMS Cartesian
{{ frag.to_string("xyz") }}
End

{% endfor %}
$ pnictogen split.ADF.in data/water-dimer.xyz
data/water-dimer_eda.in written
data/water-dimer_f1.in written
data/water-dimer_f2.in written

The above creates inputs like the following:

$ cat water-dimer_eda.in
ATOMS Cartesian
O          0.12908       -0.26336        0.64798       f=f1
H          0.89795        0.28805        0.85518       f=f1
H          0.10833       -0.20468       -0.33302       f=f1
O          0.31020        0.07569       -2.07524       f=f2
H          0.64083       -0.57862       -2.71449       f=f2
H         -0.26065        0.64232       -2.62218       f=f2
End

Fragments
f1 data/water-dimer_f1.t21
f2 data/water-dimer_f2.t21
End

$ cat water-dimer_f1.in
ATOMS Cartesian
O          0.12908       -0.26336        0.64798
H          0.89795        0.28805        0.85518
H          0.10833       -0.20468       -0.33302
End