Data mining for materials design: A computational study of single molecule magnet
Data mining for materials design: A computational study of single molecule magnet
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DOI：
10.1063/1.4862156
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发表时间：
2014-01-28
影响因子：
4.4
通讯作者：
Viet Cuong Nguyen
中科院分区：
文献类型：
--
作者：
Hieu Chi Dam;Tien Lam Pham;Viet Cuong Nguyen
提出了一种结合数据挖掘和第一性原理计算的方法来指导畸变立方烷Mn-4 + Mn-3（3+）单分子磁体的设计。该方法的基本思想是由稀疏回归和交叉验证组成的过程，用于分析材料的计算数据。该方法使我们能够证明，Mn 4+和Mn 3+离子之间的交换耦合，可以预测从组成配体的电负性和分子的结构特征的线性回归模型具有高精度。定量地、一致地评价了材料的结构特征与磁性能之间的关系，并用图表表示。讨论了材料的性能，并根据所得结果指导材料设计。(C)2014 AIP出版有限责任公司。
We develop a method that combines data mining and first principles calculation to guide the designing of distorted cubane Mn-4 + Mn-3(3+) single molecule magnets. The essential idea of the method is a process consisting of sparse regressions and cross-validation for analyzing calculated data of the materials. The method allows us to demonstrate that the exchange coupling between Mn4+ and Mn3+ ions can be predicted from the electronegativities of constituent ligands and the structural features of the molecule by a linear regression model with high accuracy. The relations between the structural features and magnetic properties of the materials are quantitatively and consistently evaluated and presented by a graph. We also discuss the properties of the materials and guide the material design basing on the obtained results. (C) 2014 AIP Publishing LLC.