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CO2RR-inverse-design

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The code used in the work “Inverse Design of Promising Alloys for Electrocatalytic CO2 Reduction via Generative Graph Neural Networks Combined with Bird Swarm Algorithm”

// repository documentation

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# CO2RR-inverse-design

The code used in the work “Inverse Design of Promising Alloys for Electrocatalytic CO<sub>2</sub> Reduction via Generative Graph Neural Networks Combined with Bird Swarm Algorithm”

## Description

This is part of the Text-to-Materials project, aimed at the inverse design of novel CO<sub>2</sub> reduction electrocatalysts.

## Getting Started

### Installing

* Clone this respostory
```
git clone https://github.com/szl666/CO2RR-inverse-design.git
cd CO2RR-inverse-design
```
* Install dependencies 
```
pip install -r requirements.txt
```

### Executing program

* To begin the inverse design process, ensure that the following model files are placed in the appropriate directories:

* Surface generation model: Place the trained CDVAE model in the slab_generation_model folder(See release).
* Adsorption energy prediction model: Place the model files for CO and H adsorption energy predictions in the adsorption_predictor_model folder.

Once the models are in place, run the following code to start the inverse design:
```
python cdvae_bsa_opt.py
```
* To analyze the inverse design results, including the average target property value per generation during BSA optimization, as well as the structural similarity between each generation and the previous one:
```
from t2mat_util.analyze_utilities import t2mat_analyze
t2mat_ana = t2mat_analyze(poscars_path='opt_poscars',training_data_path='')
```

* To analyze and visualize the inverse design results:
```
from t2mat_util.visualize_utilities import t2mat_visualize
t2mat_vis = t2mat_visualize(poscars_path='opt_poscars',if_analyze=True)
```

CO2RR-inverse-design

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