Solar-GECO: Perovskite Solar Cell Property Prediction with Geometric-Aware Co-Attention
This work addresses the slow and expensive experimental screening process for perovskite solar cells, offering a machine learning solution that improves prediction accuracy for materials science researchers, though it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the problem of predicting perovskite solar cell power conversion efficiency by proposing Solar-GECO, a geometric-aware co-attention model that integrates geometric graph neural networks and language model embeddings, achieving state-of-the-art performance with a reduction in mean absolute error from 3.066 to 2.936 compared to previous methods.
Perovskite solar cells are promising candidates for next-generation photovoltaics. However, their performance as multi-scale devices is determined by complex interactions between their constituent layers. This creates a vast combinatorial space of possible materials and device architectures, making the conventional experimental-based screening process slow and expensive. Machine learning models try to address this problem, but they only focus on individual material properties or neglect the important geometric information of the perovskite crystal. To address this problem, we propose to predict perovskite solar cell power conversion efficiency with a geometric-aware co-attention (Solar-GECO) model. Solar-GECO combines a geometric graph neural network (GNN) - that directly encodes the atomic structure of the perovskite absorber - with language model embeddings that process the textual strings representing the chemical compounds of the transport layers and other device components. Solar-GECO also integrates a co-attention module to capture intra-layer dependencies and inter-layer interactions, while a probabilistic regression head predicts both power conversion efficiency (PCE) and its associated uncertainty. Solar-GECO achieves state-of-the-art performance, significantly outperforming several baselines, reducing the mean absolute error (MAE) for PCE prediction from 3.066 to 2.936 compared to semantic GNN (the previous state-of-the-art model). Solar-GECO demonstrates that integrating geometric and textual information provides a more powerful and accurate framework for PCE prediction.