README.md

Activation energy with tunneling correction prediction with a learned VB-representation and on-the-fly quantum mechanical descriptors

This repository contains the code for a fast prediction of activation energies. Code is provided "as-is". Minor edits may be required to tailor the scripts for different computational systems. The image below shows a schematic representation of the pipeline.

Conda environment

To set up a conda environment:

conda env create -f environment.yml

Making predictions

As simple as:

python run.py --rxn_smiles 'CCO.C[CH2]>>CC[O].CC'

or

python run.py --csv_file tmp/examples.csv

The reaction smiles should be in the form of:

mol_1 + rad_2 >> rad_1 + mol_1

The first step is the prediction of relevant chemical information of reactants and products with the surrogate model, and a learned VB-representation of the reaction smiles is generated. With this, the (tunneling corrected) activation energy is predicted. With the combination of both models, a full reaction profile can be generated quickly and accurately.

Individual models

In the reactivity_model and surrogate_model directories you can find each individual model. In both folders, there is also a README in case you want to use just one part of the pipeline.

Reproducibility

We provide a script reproducibility.py to generate the main results shown in the publication. Be aware that the values concerning the Random Forest can vary in a small range. To run this script, execute:

python reproducibility.py

Citation

If you use this code, please cite:

@article{hat_predictor,
         title={Repurposing QM Descriptor Datasets for on the Fly Generation of Informative Reaction Representations: 
         Application to Hydrogen Atom Transfer Reactions}, 
         author={Javier E. Alfonso-Ramos, Rebecca M. Neeser and Thijs Stuyver},
         journal="{Digital Discovery}",
         year="{2024}",
         volume="{3}",
         issue="{5}",
         pages="{919-931}",
         doi="10.1039/D4DD00043A",
         url="https://doi.org/10.1039/D4DD00043A"
}