Accelerating Electrocatalyst Discovery through Text Mining and Multi-Objective Optimization

This repository contains the code and workflows described in the paper "Accelerating Electrocatalyst Discovery through Text Mining and Multi-Objective Optimization". The project uses natural language processing and multi-objective Pareto optimization to accelerate material discovery processes.

Getting Started

Prerequisites

• Install Conda.
• Clone this repository to your local machine.
• Ensure material systems are placed in the correct directories:
  • ORR and HER material systems:02_pareto_prediction/material_systems/MinDMaxC
  • OER material systems:02_pareto_prediction/material_systems/MaxDMinC
  • To get one of the plot (scatter plot of current density for NiPdPtRu system), put the subsets data into 02_pareto_prediction/material_systems/MinDMaxC/Ni_Pd_Pt_Ru_subset directory.

Setting Up the Environment

Use the environment.yml file to create the Conda environment:

conda env create -f environment.yml
conda activate your_env_name

Replace your_env_name with the environment name in environment.yml.

Workflow Overview

Word2Vec Model Creation

1. Navigate to the 01_word2vec_model directory and update the config.yaml file:

   • Add your Scopus API key under the APIKey entry.
   • Adjust other settings if necessary.

2. Run the Snakefile to collect papers, preprocess them, and generate a Word2Vec model:

   snakemake -c1

   The generated model will be stored in the ./model directory.

Pareto Front Predictions

1. Navigate to the 01_pareto_prediction directory.
2. Run the Snakefile:

   snakemake -c1

   The results will include Pareto front predictions for the specified material systems.

Generating Plots

1. Navigate to the 03_plots directory.
2. Run the Snakefile:

   snakemake -c1

   All plots will be generated and saved in the directory.

Generating Tables

1. Navigate to the 04_tables directory.
2. Run the Snakefile:

   snakemake -c1

   All tables will be generated and saved in the directory.

License

This project is licensed under the LGPL-3.0 License. See the LICENSE file for details.