Atomistic and Machine Learning Simulations for Nanoscale Thermal Transport

Prabudhya Roy Chowdhury, Purdue University

Abstract

The recent decades have witnessed increased efforts to push the efficiency of energy systems beyond existing limits in order to keep pace with the rising global energy demands. Such efforts involve finding bulk materials and nanostructures with desired thermal properties such as thermal conductivity (κ). For example, identifying high κ materials is crucial in thermal management of vertically integrated circuits (ICs) and flexible nanoelectronics, which will power the next generation personal computing devices. On the opposite end of the spectrum, designing ultra-low κ materials is essential for improving thermal barrier coatings in turbines and creating high performance thermoelectric (TE) devices for waste heat harvesting. In this dissertation, we identify nanostructures with such extreme thermal transport properties and explore the underlying phonon and photon transport mechanisms. Our approach follows two main avenues for evaluating potential candidates: (a) high fidelity atomistic simulations and (b) rapid machine learning-based property prediction and design optimization. The insight gained into the governing physics enables us to theoretically predict new materials for specific applications requiring high or low κ, propose accelerated design optimization pathways which can significantly reduce design time, and advance the general understanding of energy transport in semiconductors and dielectric materials. Bi2Te3, Sb2Te3 and nanostructures have long been the best TE materials due to their low at room temperatures. Despite this, computational studies such as molecular dynamics (MD) simulations on these important systems have been few, due to the lack of a suitable interatomic potential for Sb2Te3. We first develop interatomic potential parameters to predict thermal transport properties of bulk Sb2Te3. The parameters are fitted to a potential energy surface comprised of density functional theory (DFT) calculated lattice energies, and validated by comparing against experimental and DFT calculated lattice constants and phonon properties. We use the developed parameters in equilibrium MD simulations to calculate the thermal conductivity of bulk Sb2Te3at different temperatures. A spectral analysis of the phonon transport is also performed, which reveals that 80% of the total cross-plane κ is contributed by phonons with mean free paths (MFPs) between 3-100 nm. We then use MD simulations to calculate phonon transport properties such as thermal conductance across Bi2Te3 and Sb2Te3 interface, which may account for the major part of the total thermal resistance in nanostructures. By comparing our MD results to an elastic scattering model, we find that inelastic phonon-phonon scattering processes at higher temperatures increases interfacial conductance by providing additional channels for energy transport. Finally, we calculate the thermal conductivities of Bi2Te3/Sb2Te3superlattices (SLs) of varying period. The results show the characteristic minimum thermal conductivity, which is attributed to the competition between incoherent and coherent phonon transport regimes. Our MD simulations are the first fully predictive studies on this important TE system and pave the way for further exploration of nanostructures such as SLs with interface diffusion and random multilayers (RMLs). The MD simulations described in the previous section provide high-fidelity data at a high computational cost. As such, manual intuition-based search methods using these simulations are not feasible for searching for low-probability-of-occurrence systems with extreme thermal conductivity.

Degree

Ph.D.

Advisors

Ruan, Purdue University.

Subject Area

Artificial intelligence|Design|Energy|Thermodynamics

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