Open AccessTopological research on the molar magnetic susceptibility of alkali metal compounds with support vector regression
Author(s) -
Cai Cong-Zhong,
Zhuang Weiping,
Wen Yu-Feng,
Zhu Xing-Jian,
Pei Jun-Fang,
Tingting Xiao
Publication year - 2009
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.58.272
Subject(s) - magnetic susceptibility , linear regression , particle swarm optimization , topological index , alkali metal , support vector machine , mean squared error , generalization , regression analysis , regression , molar , materials science , mathematics , statistics , chemistry , computer science , physics , computational chemistry , artificial intelligence , mathematical analysis , condensed matter physics , algorithm , quantum mechanics , dentistry , medicine
According to the experimental dataset on the molar magnetic susceptibility χm of 45 alkali metal compounds and the topological descriptor?——magnetic connectivity index mF which is extracted by the magnetic valence gi of simple ion deduced from classical electrodynamics support vector regression SVR combined with particle swarm optimization for its parameter optimization is proposed to establish a model for predicting the molar magnetic susceptibility of alkali metal compound via 0F and 1F. The performance of SVR model is compared with that of multivariate linear regression MLR model. The results show that the mean absolute error the mean absolute percentage error and the root mean square error for 9-fold cross validation test of SVR models are all smaller than those achieved by MLR models. It is revealed that the generalization ability of SVR model is superior to that of MLR model. This study suggests that magnetic connectivity index is an effective descriptor and the SVR is a powerful approach to the prediction of the molar magnetic susceptibility of alkali metal compounds.