Hashemi, Amirreza
(2022)
Quantum Thermal Transport in Disordered Media using Atomistic Simulation and Machine Learning.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Topological disorder provides tremendous opportunities to design and manipulate solid materials due to added degrees of freedom to the atomistic structures. Disorder directly impacts electric, magnetic, thermal, electrical and mechanical properties. In many disordered materials, the engineering electronic properties are interlocked on understanding the relationship between the topological disorder and thermal transport. However, this requires a multidisciplinary approach that combines the structural and transport properties.
In the first phase of this thesis, we focus on thermal transport in the amorphous silicon structure. Several recent experimental and computational studies show that the thermal conductivity of amorphous silicon varies with sample size. This suggests that phonon-like propagating vibrational modes carry a significant amount of heat in amorphous silicon. In this work, we show the dependence of the propagon thermal conductivity to the structural medium- range order (MRO) which has been uncorroborated in previous studies. The results indicate that the structures with MRO show significantly larger propagon thermal conductivity than the structures without MRO. As the extent of MRO depends on the material preparation method, our study suggests that the thermal conductivity of amorphous Si also should depend on the material preparation methods.
We also tackled quantum thermal transport across grain boundaries in graphene. For disordered structures like GBs, developing a high-fidelity machine learning interatomic potential (MLIP) requires a large training dataset due to the variation of GBs and large configurational
iv
space. In this work, we present an efficient approach based on the small set of GBs to develop MLIPs while covering the entire configurational space. The simulation results unveil the interplay of dislocation density with out-of-plane buckling. We revealed the influence of GB buckling on the scattering of flexural modes. Furthermore, we lay the foundation to expand the current framework to mode resolved atomistic Green’s function in order to obtain a full phonon scattering matrix.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
10 June 2022 |
| Date Type: |
Publication |
| Defense Date: |
24 January 2022 |
| Approval Date: |
10 June 2022 |
| Submission Date: |
12 April 2022 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
140 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Computational Modeling and Simulation |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Quantum transport, Machine learning, phonon, disorder, atomistic simulation |
| Date Deposited: |
10 Jun 2022 19:23 |
| Last Modified: |
10 Jun 2022 19:23 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/42594 |
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