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dc.contributor.advisorHachmann, Johannes
dc.contributor.authorSonpal, Aditya
dc.date.accessioned2018-10-26T02:57:52Z
dc.date.available2018-10-26T02:57:52Z
dc.date.issued2018
dc.date.submitted2018-08-20 18:35:34
dc.identifier.urihttp://hdl.handle.net/10477/78667
dc.descriptionM.S.
dc.description.abstractUp until the end of the 20th century, ionic liquids and later, room temperature ionic liquids had been used as solvents for the synthesis of a number of organic and inorganic materials. Owing to their wide liquid range, immiscibility, non volatility, easy synthesis, etc., they were largely favored by scientists everywhere. Until then they were also believed to be green solvents. Their green status was later contested, which paved the way for the discovery of deep eutectic solvents (DES). These solvents were composed of two or more cheap and safe materials which associated with each other through hydrogen bond interactions to create a eutectic mixture. In other words, DES systems had a lower melting point than that of their individual components. In addition to all the advantages of ionic liquids, DES are also green solvents and can be easily and cheaply synthesized. We therefore identified the potential of DES systems and the importance of melting point in characterizing such systems. We use a data driven approach to predict melting points of DES. We first shortlist a set of 36 DES systems, each having Choline Chloride as the hydrogen bond donor. We optimize the geometries of these compounds using DFT frameworks and use that structural information to generate descriptors from the Dragon 7 software to generate our feature space. We use pearson correlation coefficients and genetic algorithm for feature selection and use the resulting feature space to train generalized linear regression algorithms from the 'scikit-learn' python library. In this thesis, we first show that the PBE0 functional with def2-TZVP basis set generates the most accurate geometrical configurations for feature representation. We further prove the supremacy of genetic algorithms over pearson correlation coefficients for feature selection. In conclusion, we show that the linear ridge regression model yields the most accurate predictions for our dataset with PBE0/def2-TZVP. We then highlight some of the challenges associated with predicting melting points and provide suggestions that could possibly help to mitigate them.
dc.formatapplication/pdf
dc.language.isoen
dc.publisherState University of New York at Buffalo
dc.rightsUsers of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.
dc.subjectMaterials Science
dc.subjectChemical engineering
dc.titlePREDICTING MELTING POINTS OF DEEP EUTECTIC SOLVENTS
dc.typeThesis
dc.typeText
dc.rights.holderCopyright retained by author.
dc.contributor.departmentChemical and Biological Engineering


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