Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we train an MPNN to first classify materials through density functional theory data from the AFLOW database as being metallic or semiconducting/insulating. We then perform a neural-architecture search to explore the model architecture and hyperparameter space of MPNNs to predict the band gaps of the materials identified as non-metals. The parameters in the search include the number of message-passing steps, latent size, and activation-function, among others. The top-performing models from the search are pooled into an ensemble that significantly outperforms existing models from the literature. Uncertainty quantification is evaluated with Monte-Carlo Dropout and ensembling, with the ensemble method proving superior. The domain of applicability of the ensemble model is analyzed with respect to the crystal systems, the inclusion of a Hubbard parameter in the density functional calculations, and the atomic species building up the materials.

翻译：基于图的神经网络，特别是消息传递神经网络（MPNN），在预测固体物理性质方面展现出巨大潜力。本研究首先训练一个MPNN，利用AFLOW数据库中的密度泛函理论数据将材料分类为金属或半导体/绝缘体。随后，我们通过神经架构搜索探索MPNN的模型架构与超参数空间，以预测被识别为非金属材料的带隙。搜索参数包括消息传递步数、潜在空间大小及激活函数等。将搜索中表现最佳的模型集成形成集合模型，其性能显著优于文献中现有模型。不确定性量化通过蒙特卡洛丢弃法与集成法进行评估，其中集成法证明更具优势。我们还分析了集合模型的适用域，涉及晶体体系、密度泛函计算中哈伯德参数的引入以及构成材料的原子种类。