OPVGCN (Fast and accurate screening framework for organic solar cells based on molecular structure and deep learning)

Introduction

A Self-Learning-Input Graph Neural Network introduces a dynamic embedding layer to accept the feedback of backpropagation during the training process and introduce the Infomax mechanism to maximize the correlation between the local features and the global features.

Dependencies

The project is built using the Python language and the following third-party frameworks:

SLI-GNN
rdkit
lightgbm 

Installation

Before downloading, please ensure that other dependencies have been installed.

First, create a new conda environment

conda create --name version python=3.7

conda install -c conda-forge rdkit,lightgbm

SLI-GNN

git clone https://github.com/Austin6035/SLI-GNN.git

Model 1

Dataset

Two datasets were used during the training of model 1 one of which is from the CEP database, which includes hundreds of thousands of molecular structures and properties: https://www.matter.toronto.edu/basic-content-page/data-download Another data set is constructed by ourselves, including 440 published opv molecular structures and PCE in /data/train.db & test.db with sqlite3 format.

Running

Training sample data and other parameter descriptions can be viewed using the following command python trainer.py -h. Combined with ray-tune, automatic parameter tune can be realized. Depending on the task type, the results will be saved in the results/regression/ or results/classification/ directory, and the loss during training will be saved in the results/ directory, and the log information during training will be saved in the log/ directory.

python trainer.py sample-dataset sample-targets

Testing

After the training is complete, the best model will be saved to the weight/ directory, and you can use test.py for testing. When testing, there can only be a material_id column in the target property file.

python test.py model_best.pth.tar sample-dataset sample-targets

Model 2

Model 2 utilizes the output of Model 1 as input, which can be easily implemented using lightgbm

Create a LightGBM dataset

train_data = lgb.Dataset(X_train, label=y_train)

Set up the LightGBM parameters params = { "objective": "binary", "metric": "binary_logloss", "boosting_type": "gbdt", "num_leaves": 31, "learning_rate": 0.05, "feature_fraction": 0.9 }

Train the LightGBM model

num_rounds = 100 model = lgb.train(params, train_data, num_rounds)

Make predictions

y_pred = model.predict(X_test)