4. Miscellaneous scripts¶

4.1. Assign charges¶

For the assignation of charges to molecular structures, fromage uses their connectivity. This is achieved through a set of tools all based on the definition of a bond from just the information in an .xyz file. Two atoms are considered bonded if their distance is below a certain threshold.

Armed with this connectivity information, the charges assigned to the atoms in one molecule can be redistributed to a whole cluster comprised of the same molecule. One important approximation is that atoms with equivalent connectivity (say the hydrogens in a methyl group) will have the same value: an average of the three original ones.

This redistribution may be useful for other applications outside of fromage, for instance for the assignation of forcefield charges. To address this, the module fro_assign_charges.py can be called as a script with as arguments the Gaussian .log file containing the molecular charges, followed by a .xyz file containing the cluster of molecules which need assignation.

In other words, the usage is:

fro_assign_charges.py mol.log clust.xyz

The output file will be called out_char by default. Several options are available including specification of the maximum bond length or the type of charge (ESP or Mulliken). More information can be found using:

fro_assign_charges.py --help

4.2. Population statistics¶

It is also often useful to quickly get some statistical information from a population analysis done in Gaussian. For this, fro_pop_stat.py can help.

Simply:

fro_pop_stat.py mol.log

This will output the maximum and minimum charge as well as the average absolute charge, the standard deviation and the total calculated energy.

4.3. Pick a molecule¶

This script selects a number of molecules from an .xyz file by indicating the atom label of on of the constituent atoms. Something similar can often easily be achieved by a visual program such as Chemcraft or VESTA, however this has the advantage of working through a terminal if it becomes necessary:

fro_pick_mol.py clust.xyz 7 13 42

This will pick out the molecules containing atoms 7, 13 and 42 from the cluster of molecules clust.xyz.

4.4. Manipulate unit cell .xyz files¶

fro_uc_tools.py can be used to produce supercells, molecular clusters and other ouputs based on the unit cell. It requires two inputs: a unit cell file in .xyz format and a vectors file like this:

8.9638004303         0.0000000000         0.0000000000
0.0000000000        10.5200004578         0.0000000000
-3.8748910079         0.0000000000         10.7924653741

There are many options to choose from so it is suggested to use fro_uc_tools -h for instructions.

For example:

fro_uc_tools.py cell.xyz vectors -r 12

Will use a unit cell geometry file (cell.xyz) and its associated lattice vectors (vectors) to produce a molecular cluster of all complete molecules with atoms falling within a radius of 12 Angstroms from the origin (-r 12). The output is called cluster_out.xyz by default.

4.5. Analyse dimers in aggregate geometries¶

Whether one is presented with a cell stemming from a periodic boundary condition calculation, or an oligomer in a localised cluster, the geometric properties of the dimers present within can help elucidate some of the intermolecular features of the system. The script fro_dimer_tools.py can identify the unique dimers in the supplied geometry, taking into account periodicity if relevant. The dimers can further be characterised by the angles between their principal, secondary and normal axes, as well as their centroid-to-centroid distance. As before, many parameters can be altered so using fro_dimer_tools.py -h is encouraged.

A suggested use is the following:

fro_dimer_tools.py clust.xyz -v -p

Will analyse an .xyz geometry file of a cluster of molecules (clust.xyz), with a verbose output (-v), and print (-p) all of the unique dimers it finds within the cluster. A file called dimers.dat will also be printed with some geometric information related to each dimer and a suggested classification, being S-S, E-F or F-F (side-by-side, edge-to-face or face-to-face).

4.6. Voronoi volume evaluation¶

It can be useful to determine the available volume of a molecule in an aggregate environment. To do this, one could use the union of the van der Waals volumes of each atom, or the Voronoi volume of the molecule, scaled by van der Waals radii.

fro_volumetrics.py clust.xyz -l 13

This will produce cube files of the available volume of the molecule containing atom 13 (-l 13) within the cluster of molecules (clust.xyz). The ouputs are the Voronoi volume (voro.cube), the van der Waals volume (vdw.cube) and the union of the two (add.cube). A file called volumes prints the integrated volume of each of the three.

4.7. Exciton coupling evaluation¶

Exciton coupling evaluation from Gaussian output files can also be carried out, using fro_coupling.py. A diabatisation of the Hamiltonian is employed which relies on the calculation of excited state properties such as population analysis or transition dipole moments.[4] More options are also available.

As an example of use, the line:

fro_coupling.py -m DIA -p TDM -mf a.log b.log -of dim_ab.log -os 2

Will use the diabatisation method (-m DIA) and use the transition dipole moment property (-p TDM) to read the Gaussian log files of monomer S₁ calculations (-mf a.log b.log) and the dimer S₂ calculation (-of dim_ab.log) with state of interest S₂ (-os 2). The output will show the diabatic Hamiltonian, whose off-diagonal elements are the exciton coupling values.

4.8. Exciton classification¶

It is sometimes useful to classify excitons as localised, delocalised or charge-transfer. To this end, the script fro_exciton_classification.py uses a Mulliken partition scheme to analyse the migration of charge density within a single excitation from a TDDFT or CIS calculation.[5] [21]

To use this, prepare an Gaussian calculation of a dimer in a given excited state using either TDDFT or CIS. Make sure that all of the atoms of one molecule (A) appear before all of the atoms of the second molecule (B). Also make sure to print the .rwf file by using the input tag %rwf=filename.rwf.

Here is an example Gaussian input for a range-separated hybrid TDDFT calculation up to the fourth excited state:

%chk=title.chk
%mem=[X]GB
%nproc=[X]
#p wb97xd 6-31g* gfprint pop=full

title

0 1
     H   0.00 0.00 0.00
     C   0.00 0.00 0.00
     .
     .
     .

--link1--
%chk=title.chk
%rwf=title
#p wb97xd 6-31g* td(nstates=4,root=4) gfprint pop=full IOP(3/33=3) geom=allcheck guess=read density=current

Now run:

fro_exciton_classification.py tddft.log tddft.rwf 3

This reads first the Gaussian log file (.log), then the read-write file (rwf) and analysises the third excited state (3). Two indices relating to the electron density migration will be printed, and a classification of the exciton will be suggested as LOC(A), LOC(B), CT A->B, CT B->A or Delocalised (localised on A, or on B, charge transfer from A to B, vice versa, or delocalised). More details can be found in the references above.