Yin Xiaoyan, Spatschek Robert, Menzler Norbert H, Hüter Claas
Institute of Energy and Climate Research IEK-2, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
Institute of Energy and Climate Research IEK-1, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany.
Materials (Basel). 2022 Apr 14;15(8):2879. doi: 10.3390/ma15082879.
Lower oxygen vacancy formation energy is one of the requirements for air electrode materials in solid oxide cells applications. We introduce a transfer learning approach for oxygen vacancy formation energy prediction for some ABO3 perovskites from a two-species-doped system to four-species-doped system. For that, an artificial neural network is used. Considering a two-species-doping training data set, predictive models are trained for the determination of the oxygen vacancy formation energy. To predict the oxygen vacancy formation energy of four-species-doped perovskites, a formally similar feature space is defined. The transferability of predictive models between physically similar but distinct data sets, i.e., training and testing data sets, is validated by further statistical analysis on residual distributions. The proposed approach is a valuable supporting tool for the search for novel energy materials.
较低的氧空位形成能是固体氧化物电池应用中空气电极材料的要求之一。我们引入了一种迁移学习方法，用于预测某些ABO3钙钛矿从双物种掺杂系统到四物种掺杂系统的氧空位形成能。为此，使用了人工神经网络。考虑到双物种掺杂训练数据集，训练预测模型以确定氧空位形成能。为了预测四物种掺杂钙钛矿的氧空位形成能，定义了一个形式上相似的特征空间。通过对残差分布的进一步统计分析，验证了预测模型在物理上相似但不同的数据集（即训练和测试数据集）之间的可迁移性。所提出的方法是寻找新型能源材料的有价值的支持工具。
