Graph Neural Network Based Macroscale AI Model for Perovskite Solar Cell Power Conversion Efficiency Prediction

Perovskite solar cells have emerged as an alternative to traditional solar cells to solve the problem of low cost-effectiveness. Perovskites, being very flexible to produce, are difficult to test for every type. Therefore, a model that predicts the performance of perovskite solar cells is imperative for further development of these materials. In this study, we create a graph-neural network-inspired artificial intelligence model that can predict the power conversion efficiency of a perovskite solar cell based on the components of the perovskite solar cells. The data was retrieved from the Perovskite Dataset Project, gathering all solar cells that had the perovskite structure ABX3. Different graph convolutional operations and aggregation algorithms were tested for the model. Overall, a model that utilized a Recurrent Neural Network with a Long Short-Term Memory implementation and a Graph Attention operation achieved a very low MAE of 1.147 and a low RMSE of 1.6971 when predicting on the un-normalized testing data set. This study demonstrates the capabilities of AI to create a macroscale perovskite solar cell prediction model and aims to serve as a baseline model for further complex models.