Date of Award

6-2019

Degree Name

MS in Polymers and Coatings

Advisor

Erik Sapper

Abstract

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest as many researchers have begun using the structure-property relationships of materials in conjunction with computational modeling to discover new materials with novel chemical and physical properties.

One such class of materials is that of emulsion polymers, which are heavily used in the coatings industry as they serve as the binder in many waterborne coating systems. The great advantage of these materials is that they are synthesized in water at high solids (30-70%) and therefore are largely compliant with stringent environmental regulations. The chemistry of these polymers is highly variant, but the predominant chemistries include copolymers of styrene and acrylic monomers such as n-butyl acrylate or copolymers of only acrylic monomers. Due to the high degree of complexity and variability of these systems, modeling their behavior according to structure-property relationships is currently impractical. Instead, this thesis will demonstrate the use of supervised machine learning methods in conjunction with genetic algorithms to predict and optimize emulsion polymer performance based on recipe composition. These emulsion polymers will also be evaluated for use in concrete coatings meant to be applied with minimal preparation work, i.e. no etching.

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