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.

翻译：钙钛矿太阳能电池是下一代光伏技术的有力候选者。然而，其作为多尺度器件的性能取决于各组成层之间复杂的相互作用。这导致了材料与器件架构组合空间的巨大复杂性，使得传统的基于实验的筛选过程既缓慢又昂贵。机器学习模型试图解决这一问题，但现有方法通常仅关注单一材料特性或忽略了钙钛矿晶体关键的几何结构信息。为解决此问题，我们提出了一种基于几何感知协同注意力（Solar-GECO）的模型来预测钙钛矿太阳能电池的功率转换效率。Solar-GECO 结合了可直接编码钙钛矿吸收层原子结构的几何图神经网络（GNN）与处理传输层及其他器件组件化学式文本字符串的语言模型嵌入表示。该模型还集成了协同注意力模块以捕捉层内依赖关系与层间相互作用，同时通过概率回归头同时预测功率转换效率（PCE）及其相关不确定性。Solar-GECO 实现了最先进的性能，显著优于多个基线模型，将 PCE 预测的平均绝对误差（MAE）从语义图神经网络（先前最优模型）的 3.066 降低至 2.936。Solar-GECO 证明，整合几何信息与文本信息能为 PCE 预测提供更强大且精确的框架。