Computational predictive models for organic semiconductors
| Autor: | Sreejith M. Nair, M. Nufail, K. R. Jinu Raj, Andrew Titus Manuel, R. Sajeev, U. C. Abdul Jaleel, R. S. Athira, M. Rakhila |
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| Rok vydání: | 2013 |
| Předmět: |
Virtual screening
Computer science Decision tree computer.software_genre Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Random forest Organic semiconductor Support vector machine Naive Bayes classifier Discriminative model Modeling and Simulation Molecular descriptor Data mining Electrical and Electronic Engineering computer |
| Zdroj: | Journal of Computational Electronics. 12:790-795 |
| ISSN: | 1572-8137 1569-8025 |
| Popis: | 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, Naive Bayes, Random forest, Support Vector Machine and Decision tree analysis in the machine learning algorithms could predict new semi-conductor molecules. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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