cathpkg-portico 2026.0.post3

Asymptotic classification of transition-state normal modes via projection onto roto-translational internal coordinates

PORTICO

Projection Onto Roto-Translational Internal COordinates

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PORTICO is a Python program for the asymptotic classification of transition-state normal modes. For a dissociation channel

R  →  TS‡  →  P1 + P2

some of the normal modes that are vibrational at the saddle point evolve asymptotically into rotations or translations of the product fragments. PORTICO identifies these transitional modes automatically, with no manual inspection of normal-mode animations and no propagation of the reaction path toward the products.

How it works

PORTICO builds a redundant internal-coordinate representation of the transition state that explicitly separates two classes of motion:

• {q}v — internal coordinates describing the vibrations of the product fragments (generated automatically for each isolated product and verified for completeness against its Cartesian frequencies);
• {q}t,r — displacement vectors representing the translations and rotations of the fragments embedded in the transition-state geometry.

The transition-state normal modes are expressed in this mixed basis by solving the Wilson GF problem, and each mode i is assigned a scalar projection Ω_i ∈ [0, 1] measuring the contribution of the roto-translational subspace. Combined with the dimensionality of the product channel, the Ω_i values identify the transitional modes — those that correlate with rotations and relative translations of the separating fragments.

Channels leading to atomic, linear, and non-linear fragments are treated on the same footing.

Requirements

• Python ≥ 3.8
• numpy, scipy, matplotlib, ase

Installation

From PyPI

pip install cathpkg-portico

From GitHub

pip install git+https://github.com/cathedralpkg/portico.git

In a conda environment

conda create -n portico python=3.11
conda activate portico
pip install cathpkg-portico

Any of the above installs the portico and gaussian2gts commands in your PATH. Alternatively, the two scripts (portico.py, gaussian2gts.py) are self-contained and can simply be downloaded and run with python3 provided the dependencies are available.

Usage

portico -h                       # help
portico --input                  # create an example input file
portico channel.inp              # run the classification
portico channel.inp --plot       # ... and plot the Omega_i weights
portico -v                       # program version

Input file

# Files with the electronic-structure data (gts format)
file_saddle   TS.gts        # transition state
file_product1 P1.gts        # product fragment 1
file_product2 P2.gts        # product fragment 2

# Thresholds (optional; default values shown)
eps_conn 1.30               # bonding threshold
eps_ccic 6.00               # frequency tolerance (cm-1)

# Atom mapping: product atom <--> TS atom (1-based)
product1:
1 1
2 2
3 3
4 4
end
product2:
1 5
2 6
end

The required data for the transition state and for each optimized product are: geometry, Cartesian Hessian, charge/multiplicity and energy, provided in gts format. The atom mapping defines which atom of the transition state corresponds to each atom of the products.

Converting Gaussian outputs to gts

The helper script gaussian2gts converts a Gaussian log file (from a freq calculation) into a gts file:

gaussian2gts TS.log        # creates TS.gts
gaussian2gts P1.log        # creates P1.gts
gaussian2gts P2.log        # creates P2.gts

If you use a different electronic-structure package, write an analogous converter producing the gts format (a simple, documented plain-text format; see the header of any generated file).

Output

PORTICO prints the Ω_i projection of every real-frequency normal mode of the transition state, sorted by weight, and identifies the transitional modes of the channel. With --plot, a bar chart of the weights is also generated:

      freq (cm^-1)   Omega_i
    --------------------------
        -1531.2        ifreq   [roto-translational]
         1266.7        0.955   [roto-translational]
         2210.0        0.954   [roto-translational]
          812.1        0.934   [roto-translational]
          715.0        0.907   [roto-translational]
         1644.3        0.706
         1166.2        0.537
         ...

Citation

If you use PORTICO in your work, please cite:

D. Ferro-Costas, PORTICO: Projection Onto Roto-Translational Internal COordinates — a program for the asymptotic classification of transition-state normal modes, submitted (2026).

License

Distributed under the MIT license. See LICENSE for details.

Author

David Ferro-Costas — Universidade de Santiago de Compostela (ORCID 0000-0002-8365-4047)