Abstract
This work shows the application of support vector machines (SVM) for modelling and prediction of zeolite synthesis, when using the gel molar ratios as model input (synthesis descriptors). Experimental data includes the synthesis results of a multi-level factorial experimental design of the system TEA:SiO2:Na2O:Al2O3:H2O. The few parameters of the SVM model were studied and the fitting performance is compared with the ones obtained with other machine learning models such as neural networks and classification trees. SVM models show very good prediction performances and generalization capacity in zeolite synthesis prediction. They may overcome overfitting problems observed sometimes for neural networks. It is also studied the influence of the type of material descriptors used as model output.
Keywords: Support vector machines, machine learning, zeolites, high-throughput synthesis, data mining
Combinatorial Chemistry & High Throughput Screening
Title: Zeolite Synthesis Modelling with Support Vector Machines: A Combinatorial Approach
Volume: 10 Issue: 1
Author(s): Jose Manuel Serra, Laurent Allen Baumes, Manuel Moliner, Pedro Serna and Avelino Corma
Affiliation:
Keywords: Support vector machines, machine learning, zeolites, high-throughput synthesis, data mining
Abstract: This work shows the application of support vector machines (SVM) for modelling and prediction of zeolite synthesis, when using the gel molar ratios as model input (synthesis descriptors). Experimental data includes the synthesis results of a multi-level factorial experimental design of the system TEA:SiO2:Na2O:Al2O3:H2O. The few parameters of the SVM model were studied and the fitting performance is compared with the ones obtained with other machine learning models such as neural networks and classification trees. SVM models show very good prediction performances and generalization capacity in zeolite synthesis prediction. They may overcome overfitting problems observed sometimes for neural networks. It is also studied the influence of the type of material descriptors used as model output.
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Cite this article as:
Manuel Serra Jose, Allen Baumes Laurent, Moliner Manuel, Serna Pedro and Corma Avelino, Zeolite Synthesis Modelling with Support Vector Machines: A Combinatorial Approach, Combinatorial Chemistry & High Throughput Screening 2007; 10 (1) . https://dx.doi.org/10.2174/138620707779802779
DOI https://dx.doi.org/10.2174/138620707779802779 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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