Date of Award
Doctor of Philosophy (PhD)
Thermal conductivity is a critical property for designing novel functional materials for engineering applications. For applications demanding efficient thermal management like power electronics and batteries, thermal conductivity is a key parameter affecting thermal designs, stability and performances of the devices. Thermal conductivity is also the critical material metrics for applications like thermal barrier coatings (TBCs) in gas turbines and thermoelectrics (TE). Therefore, thermal conductivities of various functional materials have been investigated in the past decade, but most of the materials are simple and isotropic crystals at low temperature. This is because the first-principles calculation is limited to simple crystals at ground state and few experimental methods are only capable of measuring thermal conductivity along a single direction. The objective of this thesis is to develop first-principles based atomistic modeling tools to study thermal conductivity and phonon properties of complex crystals, high temperature materials, as well as and ultrafast laser based pump-probe techniques to characterize anisotropic thermal conductivity of layered two-dimensional materials.
In the first part of this thesis, an integrated density functional theory and molecular dynamics (DFT-MD) method is developed to model the thermal conductivity and phonon properties of hybrid organic-inorganic crystals, a special kind of complex crystals integrating both organic molecules and inorganic frameworks. This DFT-MD method first develops an empirical potential field from first-principles DFT calculations, then predicts thermal conductivity using MD simulation. We applied this method to predict thermal conductivities of II-VI based hybrid crystals and organometal halide perovskites. An ultralow thermal conductivity (0.6 W/mK) is predicted in the perovskite CH3NH3PbI3, agreeing well with experimental measurements.
In the second part, instead of using empirical functional forms, a data driven machine learning algorithm is used to develop high-fidelity potential field for phonon modeling. We demonstrated that the machine learning based potential is a powerful tool for modeling phonons at high temperature, even for dynamically unstable high-temperature phases, which is a challenging problem for both empirical potential based MD and static first-principles calculations. Using a simple machine learning algorithm called Gaussian process regression, we developed potential field that can effectively capture the stabilization of BCC phase of Zirconium at 1188 K, which is predicted to be unstable using static first-principles calculations.
In the third part, a varied laser spot size technique based on time-domain thermoreflectance (TDTR) is developed to characterize anisotropic thermal conductivity. This method is applied to measure both the thermal conductivity parallel to the basal planes as well as the through-plane thermal conductivity of transition metal dichalcogenides, a group of layered two dimensional materials. Interestingly, the through-plane thermal conductivity is observed to decrease with the increasing heating frequency (modulation frequency of the pump laser) from 0.6 to 10 MHz, due to the non-equilibrium transport between different phonon modes. A two channel thermal model is developed to capture the non-equilibrium transport and to derive the thermal conductivity at local equilibrium. This finding suggest that in electronic devices working at a few GHz, the material could tend to become much more thermally insulating than steady state, raising great challenges for near junction thermal management.
Qian, Xin, "Thermal Conductivity of Complex Crystals, High Temperature Materials and Two Dimensional Layered Materials" (2019). Mechanical Engineering Graduate Theses & Dissertations. 195.