Data-driven methodologies for efficient materials calculations
Abstract/Contents
- Abstract
- As the world of electronic simulations progresses from a world where we can perform tens to thousands to millions of calculations, the way we perform science must evolve in order to most efficiently use the increasingly vast amounts of data. This thesis aims to contribute to laying the groundwork for the necessary paradigm shift. One clear aspect that must tackled in such a shift is the implementation of additional automation, automating tasks ranging from the set-up and performance of calculations to the analysis of the ensuing results. This thesis consists of three projects tackling this problem, merging databases of electronic structure calculation with machine learning and model fitting techniques. These projects are i) A procedure to algorithmically determine optimal convergence parameters, for use in varied large-scale studies, ii) Frameworks for utilizing benchmark and reference data to create post-calculation schemes which are cheap, robust, and universal, and iii) Development and benchmarking of improvements to the Drag method for transition state determination, as well as first attempts at using data to accelerate more expensive NEB calculations.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Klobuchar, Aidan J |
|---|---|
| Associated with | Stanford University, Department of Chemistry. |
| Primary advisor | Nørskov, Jens K |
| Thesis advisor | Nørskov, Jens K |
| Thesis advisor | Markland, Thomas E |
| Thesis advisor | Martinez, Todd J. (Todd Joseph), 1968- |
| Advisor | Markland, Thomas E |
| Advisor | Martinez, Todd J. (Todd Joseph), 1968- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Aidan J. Klobuchar. |
|---|---|
| Note | Submitted to the Department of Chemistry. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Aidan James Klobuchar
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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