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Ultra-high-frequency lattice vibrations, referred to as phonons, are the heat energy carriers that determine the thermal properties of materials. However, controlling phonons is particulatly challenging since phonons of a broad frequency range are excited at room temperature, making it a formidable task to control the full spectrum. In semiconductors, heat is primarily transported by THz-frequency phonons. Thus, to control phonons, features needs to be introduced at the nanometer length scale. Engineered nanoscale features offer remarkable possibilities to manipulate phonons in nanostructures. We develop strategies to stop the propagation of heat carrying phonons, or guide them along a specific direction, and thus gain unprecedented control over nanoscale thermal properties of materials. We use first-principles electronic property modeling such as density functional theory, atomistic molecular dynamics to finite element analysis, to model thermal properties of structures of different dimensions. This research will enable next-generation nanoelectronics, thermoelectric and quantum technology applications.

Thermoelectric materials convert heat to electricity and are crucial to power deep space exploration missions. Si-Ge and telluride-based thermocouples are extensively used in the radioisotope thermoelectric generators to power spacecrafts. Although thermoelectric materials have been studied for decades, their heat-to-power conversion efficiencies remain poor. To maximize the efficiency, it is essential to minimize the thermal and maximize the electronic conduction in these materials. Thermal properties of heterostructures have been studied extensively over the last decades, however, only few studies discussed their electronic properties. We investigate how strain induced by interfaces and defects affects the Seebeck coefficients. The Seebeck coefficient is a measure of the voltage induced in response to a temperature difference across a thermoelectric material, and a key factor that determines the thermoelectric performance. Our research reveals key insights into how to transform electronic properties of thermoelectric materials by strain engineering.

Heterostructured semiconductors, that contains layers of different materials, are key components for essential technologies, including telecommunication systems, light-emitting diodes, or high-electron-mobility transistors used in high-frequency devices. Nanofabrication techniques are now able to grow these materials with atomic level precision. However, the materials are strongly affected by the growth process, and their performance show high variability. It is essential to acquire a comprehensive understanding of the relationship between structural parameters and electronic properties of materials, to optimize their performance. It remains a challenge to model electronic properties of heterostructures incorporating full structural complexity. We develop data driven approaches to predict electronic properties of real experimental heterostructures.

Leading edges and control surfaces of hypersonic vehicles are exposed to extremely high heat fluxes on small tip regions that are difficult to access for active cooling (preferred designs for low drag may have tip radius smaller than 1 mm and wedge angle less than 10°, leading to surface temperatures well over 1600°C). Ultra High Temperature Ceramics (UHTCs) are the major contenders for these extreme temperature applications including the refractory carbides, nitrides and borides (e.g., (Hf, Zr, Ta)/C/N/B). Several compositions among these materials have been shown to survive the aggressive environments of hypersonic flight and solid rocket nozzles. However, their lifetimes and reliability are severely compromised by a vulnerability to high temperature oxidation as well as cracking induced by high thermal gradients. The immense configurational space offers unique opportunities for discovery of new materials with unprecedented qualities. However, discovery of materials with target properties is challenging since one needs to scan huge number of atomic configurations. We use multiple machine learning technoques to expedite the discovery process.

With the advent of space exploration, silicon electronics are being exposed to a variety of energetic particles and photons. Energetic particles tend to impart displacement damages in the material by modifying the local lattice structure and creating defects. The resulting effects may manifest as transient changes or as long-term degradation of the devices. Therefore, understanding the effects of radiation on electronic devices is particularly important for space microelectronics. However, it is difficult to quantify the extent of degradation of the materials properties due to the such damages caused by radiation. Additionally, defects are inevitably present in doped semiconductors that enable modern-day electronic, optoelectronic, or thermoelectric technologies. We investigate the relationship between the variability of structures due to radiation or fabrication and change in properties. The relationship provides guidance to accurately estimate performance of Si-based materials for various technological applications.

Coupling magnons (collective excitation of electronic spin) with phonons has received increasing attention due to its promises toward quantum information processing. The long lifetime of phonons in the current magnomechanical devices are attractive for memory applications. However, these devices are almost a millimeter wide and thus, are severely limited in their scalability toward achieving large-scale quantum memories. Additionally, they are prone to information loss due to the device geometries. We discover strategies towards improving scalability and minimzing loss of the magnomechanical devices.

Phonons, the quanta of lattice vibrations, reveal dramatic changes in their dynamics due to confinement at the nanoscale. We aim to harness emergent phononic properties to create a new paradigm for information storage and transfer, alternative to conventional charge- or spin-based computing protocols. Specifically, we hypothesize that the stimulus response of phononic ensembles can be regulated to exhibit collective dynamics, similar to neuronal activity encoded in neuroimaging data. Significant advances in the understanding of structure-processing-property relationships between nanoscale structures and phonon processes promise to help realize such an engineered ensemble.

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