Insights into oxygen vacancies from high-throughput first-principles calculations
Insights into oxygen vacancies from high-throughput first-principles calculations
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DOI：
10.1103/physrevmaterials.5.123803
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发表时间：
2021-12-27
影响因子：
3.4
通讯作者：
Oba, Fumiyasu
中科院分区：
文献类型：
--
作者：
Kumagai, Yu;Tsunoda, Naoki;Oba, Fumiyasu
氧空位对氧化材料的各种性能起着重要的作用。因此，对氧空位的深入了解有助于发现更好的氧化物材料。为了实现这一目标，我们开发了高通量点缺陷计算代码，并应用它们来表征937种氧化物中的氧空位。根据得到的大型数据集，我们分析了空位结构和地层能量，并构建了机器学习回归模型来预测空位形成能量。我们发现，根据不同的电荷状态，可以用随机森林回归模型预测空位形成能，其精度为0.27-0.44 eV。对描述符重要性的分析表明，中性空位的形成能主要由导带最小值、氧化物稳定性和带隙的轨道特性决定，而双带电缺陷的形成能则由静电能相关因素决定。这些代码和数据集是公开的，并提供图形用户界面来分析计算结果。
Oxygen vacancies play significant roles in various properties of oxide materials. Therefore, insights into the oxygen vacancies can facilitate the discovery of better oxide materials. To achieve this, we developed codes for high-throughput point-defect calculations and applied them to characterize oxygen vacancies in 937 oxides. From the resulting large dataset, we analyzed the vacancy structures and formation energies and constructed machine-learning regression models to predict vacancy formation energies. We have found that the vacancy formation energies are predicted using the random forest regression models with accuracies of 0.27-0.44 eV depending on the charge states. Analyses of the importance of the descriptors show that the formation energies of the neutral vacancies are mainly determined by the orbital characteristics of the conduction-band minima, the oxide stability, and the band gaps, whereas those of the doubly charged defects are determined by factors related to electrostatic energy. These codes and datasets are publicly available, and a graphical user interface is available to analyze the calculation results.