DSTAR : Dft & STructure free Active motif based Representation

This repository contains codes and notebooks used to create results in our paper.

For more details, check out this paper (https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.1c00726).

Table of Contents

• Prerequisites
• Usage

Prerequisites

• Generalized Adsorption Simulator for Python (GASpy)

• Additional packages required for gaspy enviroment:

• scikit-learn (0.24.2)
• pymatgen (2021.3.3)

Usage of ML model

See DSTAR_Guide_.pdf.

Application

DSTAR can be utilized to discover catalyst for various electrochemical reactions. You can reproduce the catalyst discovery for each reaction with the following descriptions.

CO2RR

Usage

To reproduce DSTAR application for CO2RR, please refer to three ipynb files in application/CO2RR/. Each ipynb file will do the following:

01_Scaler.ipynb will generates scaler to normalize the productivity.

02_Heatmap.ipynb will visualize productivty heatmap.

03_Selectivity_Plot.ipynb will plot the productivity for each product corresponding to applied potential, composition and coordination number.

More details are in each ipynb files.

Data

All predicted CO* / H* / OH* binding energies and coordination number of prototype surface used for application can be found in application/CO2RR/data/energy and application/CO2RR/data/CN_dict.pkl, and the boundary conditions of selectivity map are in application/CO2RR/script/condition.py.