FPtand is a folder containing the FPtand model that is using Text-CNN to generate molecular embedding based on donor and acceptor pair data with Mogan fingerprint in organic solar cell for predicting PCE.
The model is stored in model8_fp, and the files fa_fp.npy and fd_fp.npy are training data of acceptor and donor, respectively.
fp_Y.npy is the file containing target PCE values, and fp1.py is the main program code.
FPpara is a folder containing a similar model as FPtand except that manipulates the donor and acceptor data in parallel to make a final prediction.
MLemb includes four baseline manchine learning models: RF, SVR, Xgboost and gcforest. The inputs for the training and test of models are featuremap_train.csv and featuremap_test.csv, respectively.
fingerprint includes FPtand models with embedding from four types of fingerprints, namely APfp, CDKfp, GRAPHfp and MACCS.
visualization consists of t-SNE and Shap analyses for embedding.
Configuring the environment according to FPtand.yml before running the code.