Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations
Machine learning models for predicting the dielectric constants of oxides based on high-throughput first-principles calculations
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DOI：
10.1103/physrevmaterials.4.103801
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发表时间：
2020-10
影响因子：
3.4
通讯作者：
Akira Takahashi;Y. Kumagai;J. Miyamoto;Yasuhide Mochizuki;F. Oba
中科院分区：
文献类型：
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作者：
Akira Takahashi;Y. Kumagai;J. Miyamoto;Yasuhide Mochizuki;F. Oba
电子和离子的贡献的静态介电常数的预测模型已被构建使用的数据从密度泛函微扰理论计算约1200金属氧化物通过监督机器学习。我们开发了两种类型的随机森林回归模型的基态晶体结构的氧化物：一个模型只需要组成信息和其他模型也使用结构信息。虽然训练数据包括各种原子框架，但即使仅使用成分信息作为特征描述符，预测模型也表现良好。在预测的电子贡献的介电常数，具有和不具有结构信息的回归模型的精度是可比的，而结构描述符更清楚地提高了预测精度的离子的贡献。我们还分析了特征对介电常数预测的重要性。的平均原子质量和质量密度被确定为显着的功能，在预测的电子贡献没有和结构信息，分别。主量子数和平均近邻距离变化的标准偏差被认为是重要的离子贡献的各自的预测模型。介电常数和这些功能之间的相关性进行了讨论，沿着与基本的物理机制。
Prediction models of both the electronic and ionic contributions to the static dielectric constants have been constructed using data from density functional perturbation theory calculations of approximately 1200 metal oxides via supervised machine learning. We developed two types of random forest regression models for oxides with the ground-state crystal structures: one model requires only compositional information and the other model also uses structural information. Although the training data included various atomic frameworks, the prediction models performed well even when only compositional information was used as feature descriptors. In prediction of the electronic contributions to the dielectric constants, the accuracies of the regression models with and without structural information were comparable, while the structural descriptors more clearly improved the prediction accuracy for the ionic contributions. We also analyzed the feature importance for prediction of the dielectric constants. The mean atomic mass and mass density were determined to be significant features in prediction of the electronic contributions without and with structural information, respectively. The standard deviation of the principal quantum number and mean neighbor distance variation were found to be important for the respective prediction models of the ionic contributions. The correlations between the dielectric constants and these features are discussed, along with the underlying physical mechanisms.