Role of descriptors in predicting the dissolution energy of embedded oxides and the bulk modulus of oxide-embedded iron
Role of descriptors in predicting the dissolution energy of embedded oxides and the bulk modulus of oxide-embedded iron
复制标题
描述符在预测嵌入氧化物的溶解能和嵌入氧化物的铁的体积模量中的作用
DOI：
10.1103/physrevb.95.014101
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发表时间：
2017
影响因子：
3.7
通讯作者：
Tanaka Yuzuru
中科院分区：
文献类型：
--
作者：
Takahashi Keisuke;Tanaka Yuzuru
从第一性原理计算和数据挖掘两个方面对氧化物包埋大块铁进行了研究。29个氧化物被嵌入到铁的空位中，在那里执行第一原理计算，并将结果存储为数据集。使用机器学习对铁中氧化物的溶解能和嵌入氧化物的铁的体积模数进行预测。特别是，实现了支持向量机(SVM)和线性回归(LR)，其中揭示了确定溶解能和体积弹性模量的描述符。利用训练好的支持向量机和支持向量机，实现了不同氧化物在铁中溶解能的预测和反问题--从期望的体积模数导出相应的描述变量--。还揭示了所选择的描述符背后的物理起源，其中在多维空间中操纵每个单独的描述符允许预测溶解能和体积模数。因此，如果确定了适当的描述符，对物理现象的预测原则上是可以实现的。
Oxide-embedded bulk iron is investigated in terms of first principles calculations and data mining. Twenty-nine oxides are embedded into a vacancy site of iron where first principles calculations are performed and the resulting calculations are stored as a data set. A prediction of the dissolution energy of oxides within iron and the bulk modulus of oxide-embedded iron is performed using machine learning. In particular, support vector machine (SVM) and linear regression (LR) are implemented where descriptors for determining the dissolution energy and bulk modulus are revealed. With trained SVM and LR, the prediction of the dissolution energy for different oxides in iron and the inverse problem—deriving the corresponding descriptor variables from a desired bulk modulus—are achieved. The physical origin behind the chosen descriptors is also revealed where manipulating each individual descriptor within a multidimensional space allows for the prediction of the dissolution energy and bulk modulus. Thus, predictions of physical phenomena are, in principle, achievable if the appropriate descriptors are determined.