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Property Phase Diagrams for Compound Semiconductors through Data Mining

机译:通过数据挖掘为化合物半导体提供性能相图

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摘要

This paper highlights the capability of materials informatics to recreate “property phase diagrams” from an elemental level using electronic and crystal structure properties. A judicious selection of existing data mining techniques, such as Principal Component Analysis, Partial Least Squares Regression, and Correlated Function Expansion, are linked synergistically to predict bandgap and lattice parameters for different stoichiometries of GaxIn1−xAsySb1−y, starting from fundamental elemental descriptors. In particular, five such elemental descriptors, extracted from within a database of highly correlated descriptors, are shown to collectively capture the widely studied “bowing” of energy bandgaps seen in compound semiconductors. This is the first such demonstration, to our knowledge, of establishing relationship between discrete elemental descriptors and bandgap bowing, whose underpinning lies in the fundamentals of solid solution thermodyanamics.
机译:本文着重介绍了材料信息学利用电子和晶体结构特性从元素层面重建“特性相图”的能力。从基本元素描述符开始,明智地选择现有数据挖掘技术,例如主成分分析,偏最小二乘回归和相关函数展开,以协同预测GaxIn1-xAsySb1-y不同化学计量的带隙和晶格参数。特别是,从高度相关的描述符数据库中提取的五个此类元素描述符显示出可以共同捕获化合物半导体中广泛研究的能带隙“波动”。据我们所知,这是首次建立离散元素描述子与带隙弯曲之间关系的论证,其基础在于固溶体热动力学。

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