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Machado J. A. Tenreiro Rocha-Neves J. M. Azevedo Filipe Andrade J. P. 《Nonlinear dynamics》2021,106(2):1525-1555
Nonlinear Dynamics - Given a data-set of Ribonucleic acid (RNA) sequences we can infer the phylogenetics of the samples and tackle the information for scientific purposes. Based on current data and... 相似文献
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Dr. Antonio M. Rodríguez Dr. Pilar Prieto Prof. Antonio de la Hoz Prof. Ángel Díaz-Ortiz D. Raúl Martín Prof. José I. García 《ChemistryOpen》2015,4(3):308-317
The aim of this work was to determine the parameters that have decisive roles in microwave-assisted reactions and to develop a model, using computational chemistry, to predict a priori the type of reactions that can be improved under microwaves. For this purpose, a computational study was carried out on a variety of reactions, which have been reported to be improved under microwave irradiation. This comprises six types of reactions. The outcomes obtained in this study indicate that the most influential parameters are activation energy, enthalpy, and the polarity of all the species that participate. In addition to this, in most cases, slower reacting systems observe a much greater improvement under microwave irradiation. Furthermore, for these reactions, the presence of a polar component in the reaction (solvent, reagent, susceptor, etc.) is necessary for strong coupling with the electromagnetic radiation. We also quantified that an activation energy of 20–30 kcal mol−1 and a polarity (μ) between 7–20 D of the species involved in the process is required to obtain significant improvements under microwave irradiation. 相似文献
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Spyridon Koutsoukos Frederik Philippi Francisco Malaret Tom Welton 《Chemical science》2021,12(20):6820
There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids. 相似文献
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Guizellini Filipe Corrêa Franzin Bruno Trevizan da Silva Matheus Antonio Abra Luciana Mazotti Hojo Ossamu Pastre Iêda Aparecida Ribeiro Clóvis Augusto Paiva-Santos Carlos de Oliveira Fertonani Fernando Luis 《Journal of Thermal Analysis and Calorimetry》2021,143(1):47-60
Journal of Thermal Analysis and Calorimetry - The aim of this work is to synthesize and characterize a new structured silver–clay dried, calcined or sintered at different temperatures... 相似文献
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