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Quantum Thermal Transport in Disordered Media using Atomistic Simulation and Machine Learning

Hashemi, Amirreza (2022) Quantum Thermal Transport in Disordered Media using Atomistic Simulation and Machine Learning. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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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
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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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Hashemi, Amirrezaamh299@pitt.eduamh299
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairLee, Sangyeopsylee@pitt.edusylee
Committee MemberJohnnson, KarlKarlj@pitt.edukarlj
Committee MemberWang, Guofengguw8@pitt.eduguw8
Committee MemberMcGaughey,
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


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