This repo holds the source data and codes for the research paper entitled TBC.

To reproduce the results, you need using python3 and relevant packages including pytorch, scikit-learn, pandas, matplotlib etc. You can build up the environment for this repo and reproduce the results following the steps:

1. Install Anaconda or Miniconda.

   Alternatively, you can firstly install python (version 3.8) and then use pip to install necessary packages.

2. Clone this repository to your local disk:

git clone https://github.com/755452800/DACs_Screening_for_CO2RR_using_ML.git

3. Change to the project directory, create the python environment and activate the environment:

cd DACs_Screening_for_CO2RR_using_ML
conda env create -f environment.yml
conda activate DACs_Screening

Alternatively, you can use pip to install packages to build the environment after installing python:

cd DACs_Screening_for_CO2RR_using_ML
pip install -r requirements.txt

4. Check the source data and codes and run the scripts under the DACs_Screening environment to reproduce the results.

Note that plt.show() in the scripts is a blocking function, so you need manually close the figures to continue if you're running the scripts in a terminal.

TBC