基于架构优化的消息传递神经网络的带隙回归
Band-gap regression with architecture-optimized message-passing neural networks
AI总结：
本研究训练消息传递神经网络分类金属与非金属，并通过神经架构搜索优化模型超参数以预测非金属材料带隙，构建的集成模型性能显著优于现有模型并进行了不确定性量化与适用域分析。
摘要：
基于图的神经网络，特别是消息传递神经网络（MPNNs），在预测固体物理性质方面展现出巨大潜力。在本研究中，我们训练了一个MPNN，首先利用来自AFLOW数据库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，我们执行神经架构搜索，以探索MPNN的模型架构和超参数空间，从而预测被识别为非金属材料的带隙。搜索中的参数包括消息传递步数、潜在大小和激活函数等。搜索中表现最佳的模型被汇集到一个集成模型中，其性能显著优于文献中的现有模型。通过Monte-Carlo Dropout和集成方法评估不确定性量化，其中集成方法证明更为优越。集成模型的适用域还针对晶体系统、密度泛函计算中Hubbard参数的包含情况以及构成材料的原子种类进行了分析。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
