Computational predictive models for organic semiconductors

Authors:
R. Sajeev;R. S. Athira;M. Nufail;K. R. Jinu Raj;M. Rakhila;Sreejith M. Nair;U. C. Abdul Jaleel;Andrew Titus Manuel
Affiliations:
Centre for Cheminformatics, Department of Chemistry, Malabar Christian College, Calicut, India;Centre for Cheminformatics, Department of Chemistry, Malabar Christian College, Calicut, India;Centre for Cheminformatics, Department of Chemistry, Malabar Christian College, Calicut, India;Centre for Cheminformatics, Department of Chemistry, Malabar Christian College, Calicut, India;Centre for Cheminformatics, Department of Chemistry, Malabar Christian College, Calicut, India;Centre for Cheminformatics, Department of Chemistry, Malabar Christian College, Calicut, India;Centre for Cheminformatics, Department of Chemistry, Malabar Christian College, Calicut, India;Department of Chemistry, Madras Christian College, Madras, India
Venue:
Journal of Computational Electronics
Year:
2013

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Abstract

Virtual screening methods were adopted for modeling and prediction of semi conductivity of Schiff base molecules. The predictive models built using data mining methods that were generated from descriptor based technology was able to give an alternative method to the currently used HOMO-LUMO gap based prediction methodologies. The predictions using the discriminative classifiers such as, Naïve Bayes, Random forest, Support Vector Machine and Decision tree analysis in the machine learning algorithms could predict new semi-conductor molecules.