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Machine learning for chemical engineering applications in computational chemistry, materials design, education, and disease modeling

URL to cite or link to: http://hdl.handle.net/1802/35881

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PDF of dissertation
Thesis (Ph. D.)--University of Rochester. Department of Chemical Engineering, 2020.
Machine learning (ML) is a broad, flexible suite of applied statistics tools combined with optimization algorithms, made possible by the advent of modern computing power. The strength of ML methods is limited by data availability and complexity of the task to learn, but nonetheless, ML has seen extensive use in the fields of medicine, technology, and engineering. It also has applications in chemical engineering and computational chemistry. In this thesis, I present various such applications. Using ML for computer vision, I helped make a mixed-reality educational tool for chemical engineering undergraduates, which led to further work on a virtual-reality app for high school chemistry students. I have also used ML methods for functional design of antimicrobial peptides, and investigated ways to address the problem of dataset sizes in this setting, to increase the chances of its use in experimental laboratory settings, where data is scarce. I also helped develop a framework for ML in molecular dynamics simulations to reduce the barrier of entry for non-experts in ML. Finally, my most recent work has been toward applying biasing methods from statistical mechanics to COVID-19 disease modeling to improve model prediction without parameter fitting. The broad range of applications presented here only scratches the surface of what can be done with the powerful combination of traditional numerical models augmented by machine learning methods, serving to illustrate how much can be done with its wide adoption in the field of chemical engineering, and the sciences as a whole.
Contributor(s):
Rainier Barrett - Author
ORCID: 0000-0002-5728-9074

Andrew D. White (1987 - ) - Thesis Advisor

Primary Item Type:
Thesis
Identifiers:
Local Call No. AS38.695
Language:
English
Subject Keywords:
Augmented reality; Education; Machine learning
Sponsor - Description:
National Science Foundation (NSF) - #1751471, #1764415, and #2029095
First presented to the public:
10/12/2020
Originally created:
2020
Original Publication Date:
2020
Previously Published By:
University of Rochester
Place Of Publication:
Rochester, N.Y.
Citation:
Extents:
Number of Pages - xiv, 125 pages
Illustrations - color illustrations
License Grantor / Date Granted:
Marcy Strong / 2020-10-12 10:15:44.934 ( View License )
Date Deposited
2020-10-12 10:15:44.934
Date Last Updated
2020-10-12 10:18:10.383
Submitter:
Marcy Strong

Copyright © This item is protected by copyright, with all rights reserved.

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