Machine learning and artificial intelligence are increasingly becoming mainstream in our daily lives, from smart algorithms that recognize us online to cars that can drive themselves. In this defense, the intersection of machine learning and computational chemistry are applied to the generation of new PFAS molecules that are less toxic than those currently used today without sacrificing the unique properties that make them desirable for industrial use. Additionally, machine learning is used to complete the SAMPL6 logP challenge and to correlate molecules to best DFT functionals for enthalpies of formation.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1944346 |
| Date | 05 1900 |
| Creators | Kuntz, David Micah |
| Contributors | Wilson, Angela, Cundari, Thomas, Acree, William, Marshall, Paul, Ma, Shengqian |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Kuntz, David Micah, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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