Before we start#
History#
The original URVA method was implemented by Zoran Konkoli in the link L716 of Gaussian package. However, this part is never incorporated into the public version of Gaussian. Since then, many other contributors including Dr. Wenli Zou added functionalities into this part and migrated this part from older version of Gaussian into newer version of Gaussian for several times. As Gaussian package was written in Fortran 77, the corresponding URVA part was written in the same language.
In 2015, Dr. Dieter Cremer and Dr. Elfi Kraka thought about a stand-alone version of the URVA program. Yunwen Tao, a Ph.D. student of CATCO at that time started this project using Fortran 90. Then he switched to Python, which is more flexible and easier to use. This led in 2018 to the first stand-alone-version called pURVA. The code has in between been completely overworked and optimized, as well as new features have been added by Dr. Robert Kalescky, leading to a modern version named URVA2025, the current release.
Execution of URVA#
The proper execution of URVA requires modern Python3 interpreter.
Here are the list of Python modules needed to run URVA: (1) NumPy, (2) SciPy, (3) SymPy, (4) sys, (5) os, (6) copy, (7) gc, (8) math and (9) time.
Make sure that all these modules have been installed properly.
URVA expects and then reads in an external text file as the user input file. After this file is prepared, in the terminal, type in
$ python main.py``myinputfile``
From the standard output, we could monitor how the calculation goes. The calculation results will be dumped into external text files on the disk.
To make life easier, running URVA on ManeFrame cluster is recommended as URVA has been developed and tested on the same machine. Before running URVA, remember to load the Python interpreter by using
$ module load python
Theory as Unified Reaction Valley Approach#
The name of “Unified Reaction Valley Approach” firstly appeared on scientific journals in 1997 when Konkoli, Kraka and Cremer published their comprehensive studies of CH\(_3\) + H\(_2\) \(\rightarrow\) CH\(_4\) + H on J. Phys. Chem. A[Konkoli et al., 1997]. In that paper, one of the highlights is to introduce the approach that calculates the adiabatic mode coupling coefficient that is decomposition of reaction path curvature into adiabatic local modes which was a novel approach dealing with vibrational spectroscopy. URVA is based on the Reaction Path Hamiltonian(RPH) that was intensively developed by Miller, Handy[Miller et al., 1980], Page and McIver[Page and McIver, 1988]. In the year of 2011, Dr. Kraka published a well-written review on the relationship between RPH and URVA[Kraka, 2011]. Most recently, Dr. Zou proposed a new approach to decompose the reaction path direction and curvature into internal coordinates which opens the possibility to study chemical reactions in large systems, e.g. organometallic compounds and enzymes[Zou et al., 2016].
One of the most important papers involved in URVA is the introduction of Diabatic Mode Ordering(DMO) procedure which has now been widely used in several projects within CATCO group[Konkoli et al., 1997].