High-throughput quantum chemical validation of machine learning on solid-state batteries and sustainable plastics
Abstract/Contents
- Abstract
- My work applied a vast array of materials discovery techniques to identify polymers which would be suitable for biodegradation and recycling and materials for the improved performance of solid-state batteries. New chemistries in both of these fields will allow for optimization to specific tasks without compromise in performance. Chemically recyclable plastics are a necessary step in order to minimize our feedstock usage which drains earth resources and solid-state batteries are an avenue to bring lithium metal batteries to market. In my attempts to quantify the kinetic barriers for polymers, I found that most techniques are not fast enough to screen a wide field of polymers accurately. Through a tight-binding-density-functional-theory protocol I found patented polycarbonates which can undergo the entropically controlled ring-closing depolymerization for infinite chemical recycling. To mitigate mechanical failure for batteries, I found a zero-strain cathode NbFe3(PO4)6 that has a theoretical capacity orders of magnitude larger than current cathode materials. I also developed a parameter to quickly calculate adhesion between two solid materials and identified electrolyte coating materials which could increase surface contact for lower interfacial resistance. In addition I quantified errors between experimental and computational values of diffusion in solid electrolyte materials. The range of data science, quantum, and classical methods have limitations and trade offs with speed, accuracy, and breadth of application, which I discuss extensively to qualify all presented results. This work contributes to accelerating the commercialization of necessary environmentally-friendly products by presenting a range of viable options to be further optimized by experimentalists.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ransom, Brandi |
|---|---|
| Degree supervisor | Qin, Jian |
| Degree supervisor | Reed, Evan |
| Thesis advisor | Qin, Jian |
| Thesis advisor | Reed, Evan |
| Thesis advisor | Devereaux, Thomas |
| Thesis advisor | Sendek,Austin |
| Degree committee member | Devereaux, Thomas |
| Degree committee member | Sendek,Austin |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Materials Science and Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Brandi Ransom. |
|---|---|
| Note | Submitted to the Department of Materials Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/zt049rp1371 |
Access conditions
- Copyright
- © 2023 by Brandi Ransom
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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