Using support vector regression for the prediction of the band gap and melting point of binary and ternary compound semiconductors

In this work, atomic parameters support vector regression (APSVR) was proposed to predict the band gap and melting point of III–V, II–VI binary and I–III–VI 2, II–IV–V 2 ternary compound semiconductors. The predicted results of APSVR were in good agreement with the experimental ones. The prediction...

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Veröffentlicht in:	Solid state sciences 2006-02, Vol.8 (2), p.129-136
Hauptverfasser:	Gu, Tianhong, Lu, Wencong, Bao, Xinhua, Chen, Nianyi
Format:	Artikel
Sprache:	eng
Schlagworte:	Atomic parameters Band gap Condensed matter: electronic structure, electrical, magnetic, and optical properties Electron density of states and band structure of crystalline solids Electron states Exact sciences and technology Melting point Physics Semiconductor Semiconductor compounds Support vector regression
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container_start_page	129
container_title	Solid state sciences
container_volume	8
creator	Gu, Tianhong Lu, Wencong Bao, Xinhua Chen, Nianyi
description	In this work, atomic parameters support vector regression (APSVR) was proposed to predict the band gap and melting point of III–V, II–VI binary and I–III–VI 2, II–IV–V 2 ternary compound semiconductors. The predicted results of APSVR were in good agreement with the experimental ones. The prediction accuracies of different models were discussed on the basis of their mean error functions ( MEF) in the leave-one-out cross-validation. It was found that the performance of APSVR model outperformed those of back propagation-artificial neural network (BP-ANN), multiple linear regression (MLR) and partial least squares regression (PLSR) methods.
doi_str_mv	10.1016/j.solidstatesciences.2005.10.011
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subjects	Atomic parameters Band gap Condensed matter: electronic structure, electrical, magnetic, and optical properties Electron density of states and band structure of crystalline solids Electron states Exact sciences and technology Melting point Physics Semiconductor Semiconductor compounds Support vector regression
title	Using support vector regression for the prediction of the band gap and melting point of binary and ternary compound semiconductors
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