Date of Graduation
Summer 2021
Degree
Master of Science in Materials Science
Department
Physics, Astronomy, and Materials Science
Committee Chair
Ridwan Skidja
Abstract
In this study, an important aspect of the synthesis process for a-BxC:Hy was systematically modeled by utilizing the Reactive Molecular Dynamics (MD) in modeling the argon bombardment from the orthocarborane molecules as the precursor. The MD simulations are used to assess the dynamics associated with the free radicals that result from the ion bombardment. By applying the Data Mining/Machine Learning analysis into the datasets generated from the large reactive MD simulations, I was able to identify and quality the kinetics of these radicals. Overall, this approach allows for a better understanding of the overall mechanism at the atomistic level of Ar bombardment and the role of radical species towards the formation of the orthocarborane network and in turn the boron carbide thin films
Keywords
orthocarboranes, boron carbide, reactive molecular dynamics simulations, data mining, machine learning
Subject Categories
Artificial Intelligence and Robotics | Atomic, Molecular and Optical Physics | Other Physical Sciences and Mathematics | Quantum Physics
Copyright
© Kwabena Asante-Boahen
Recommended Citation
Asante-Boahen, Kwabena, "Modeling of Argon Bombardment and Densification of Low Temperature Organic Precursors Using Reactive MD Simulations and Machine Learning" (2021). MSU Graduate Theses/Dissertations. 3669.
https://bearworks.missouristate.edu/theses/3669
Open Access
Included in
Artificial Intelligence and Robotics Commons, Atomic, Molecular and Optical Physics Commons, Other Physical Sciences and Mathematics Commons, Quantum Physics Commons
