Density functional theory and machine learning methods for dielectric materials discovery
Abstract/Contents
- Abstract
- Dielectrics are an important class of materials that are ubiquitous in modern electronic applications. Even though their properties are important for the performance of devices, the number of compounds with known dielectric constant is on the order of a few hundred. In this thesis, Density Functional Perturbation Theory (DFPT) is used to screen for the dielectric constant and refractive index of materials in a fast and computationally efficient way. By benchmarking against experiments, it is found that DFPT can in general predict the experimental dielectric constant with a deviation of less than 25%. Despite some discrepancies between DFPT results and reported experimental values, the high-throughput methodology is found to be useful in identifying interesting compounds by ranking. This is demonstrated by the high Spearman correlation factor (0.92). It is also demonstrated that DFPT provides a good estimate for the refractive index of a compound without calculating the frequency dependence of the dielectric matrix (MARD=5.7%). By applying the screening methodology, the largest publicly available dielectric tensors database to date is obtained containing 1,056 compounds. Finally, a machine learning model that is able to predict the electronic dielectric constant is developed as an alternative screening method. The average prediction error came out as less than 18\% for the dielectric constant and less than 9% for the refractive index. Given the high Spearman correlation factor (0.89), such a model can be used as an even faster materials screening method.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Petousis, Ioannis, Mr |
|---|---|
| Associated with | Stanford University, Department of Materials Science and Engineering. |
| Primary advisor | Persson, Kristin A, 1971- |
| Primary advisor | Prinz, F. B |
| Thesis advisor | Persson, Kristin A, 1971- |
| Thesis advisor | Prinz, F. B |
| Thesis advisor | McIntyre, Paul Cameron |
| Thesis advisor | Reed, Evan J |
| Advisor | McIntyre, Paul Cameron |
| Advisor | Reed, Evan J |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Ioannis Petousis. |
|---|---|
| Note | Submitted to the Department of Materials Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Ioannis Petousis
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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