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(Notes: this package is just used for reproducing paper1 and paper2 results. An up-to-date version can be found in TCIT-thermo folder in https://github.com/zhaoqy1996/TCIT_thermo. We'll continue update new component additivity values (CAVs) and ring corrections (RCs) and also add TCIT predictions for other properties and radicals species in TCIT_thermo project in a near future.

TCIT

TCIT, the short of Taffi component increment theory, is a powerful tool to predict thermochemistry properties, like enthalpy of formation.

This script implemented TCIT which performs on a given folder of target compounds based on a fixed TCIT CAV database distributed with the paper "A Self-Consistent Component Increment Theory for Predicting Enthalpy of Formation" by Zhao and Savoie. Further ring correction is added distributed with the paper "Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds" by Zhao, Iovanac and Savoie.

The script operates on either a folder of xyz files or a list of smiles strings, prints out the Taffi components and corresponding CAVs that are used for each prediction, and returns the 0K and 298K enthalpy of formation.

Software requirement

  1. openbabel 2.4.1 or higher
  2. anaconda

Set up an environment if needed

  • conda create -n TCIT -c conda-forge python=3.7 rdkit
  • source activate TCIT
  • pip install alfabet

Usage

If your input type a xyz file:

  1. Put xyz files of the compounds with research interest in one folder (default: inputxyz)
  2. Type "python TCIT.py -h" for help if you want specify the database files and run this program.
  3. By default, run "python TCIT" and the program will take all xyz files in "input_xyz" and return a prediction result in result.log

If your input type is smiles string:

  1. Make a list of smiles string (default: input.txt)
  2. Type "python TCIT.py -h" for help if you want specify the database files and run this program.
  3. By default, run "python TCIT -t smiles" and the program will take all smiles string in input.txt and return a prediction result in result.log

Notes

  1. Make sure the bin folder of openbabel is added in the environment setting, or 'alias obabel=' to that bin folder