This repository contains a model based on the two-layer feedforward artificial neural network for predicting the power conversion efficiency (PCE). This work demonstrates the possibility to train a neural network using descriptors for predicting photovoltaic properties.
The neural network model consists of two fully-connected layers with ReLU activations. The dimensionality of the layers is shown below:
As a loss function we use the mean squared error. The model has been trained using Adam optimizer.
The squared correlation coefficient for the test set equals 0.83. The example of usage and the evaluation of the trained model is implemented in the script model_test.py. To run this script, one has to install all requirements by, for instance, invoking the command: pip install -r requirements.txt.