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.
|Advisor:||White, Andrew D.|
|Commitee:||Porosoff, Marc, Franco, Ignacio, Bai, Zhen|
|School:||University of Rochester|
|Department:||Hajim School of Engineering and Applied Sciences|
|School Location:||United States -- New York|
|Source:||DAI-B 82/4(E), Dissertation Abstracts International|
|Subjects:||Chemical engineering, Statistical physics, Computational chemistry, Artificial intelligence|
|Keywords:||Augmented reality, Education, Machine learning|
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