title: Zeolite synthesis modelling with support vector machines: a combinatorial approach.
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.
authors.affiliation: Instituto de Tecnología Química (UPV-CSIC), Av. de los Naranjos s/n, E-46022 Valencia, Spain.
authors.forename: Jose Manuel
authors.initials: JM
authors.lastname: Serra
authors.forename: Laurent Allen
authors.initials: LA
authors.lastname: Baumes
authors.forename: Manuel
authors.initials: M
authors.lastname: Moliner
authors.forename: Pedro
authors.initials: P
authors.lastname: Serna
authors.forename: Avelino
authors.initials: A
authors.lastname: Corma
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008956:Models, Chemical; D016571:Neural Networks, Computer; D044367:Phase Transition; D017641:Zeolites / Q000138:chemical synthesis*
chemical_list: D017641:Zeolites