MSE Department Seminar

My research group’s expertise lies in the development of physics-based molecular models and simulation methods as well as data-driven machine learning models for designing and characterizing soft macromolecular materials. In the past few years we have devoted significant efforts towards the development of machine learning based computational methods to accelerate and automate interpretation of structural characterization data from scattering and microscopy techniques.

Metallic delafossites such as PdCoO2 are layered oxides with a unique crystal structure: conductive Pd+ sheets alternate with insulating [CoO2]− layers. This layered crystal structure gives rise to two remarkable features. First, the Pd+ layers host quasi–two-dimensional electrons with conductivities comparable with elemental metals, enabling long mean free paths and unusual transport phenomena even in ultrathin films. Second, the polar stacking of charged layers produces surfaces and interfaces with strongly contrasting electronic properties.

Aluminum nitride (AlN) is widely used as the piezoelectric layer for RF acoustic filters for its high electromechanical coupling coefficient, high acoustic phase velocity, as well as low acoustic and dielectric loss. In addition to being an excellent piezoelectric, AlN is also an ultra-wide bandgap semiconductor used in UV photonics and RF transistor amplifiers. An exciting opportunity enabled by Bulk Acoustic Wave (BAW) filters is the monolithic integration with active devices such as high-electron-mobility-transistor (HEMT) amplifiers.

Over the past decades, we have witnessed tremendous progress and success in engineered photonic materials, including photonic crystals, plasmonic nanostructures, and metamaterials. For instance, by tailoring the geometry of the building blocks of metamaterials and engineering their spatial distribution, we can control the amplitude, polarization state, phase, and trajectory of light in an almost arbitrary manner [1,2].

Technological developments often rely on specifically designed materials and molecules. The increasing pace of technology development, coupled with rising energy needs and climate challenges, requires faster approaches for materials discovery. Historically, materials have been discovered by trial-and-error approaches that rely on chemical intuition. Designing materials with tailored properties is challenging because of the astronomical number of possible compounds and structures, and materials behaviors that do not adhere to standard chemical intuition.

For more than 50 years the Los Alamos Neutron Science Center (LANSCE) has provided the scientific underpinnings in nuclear physics and material science needed to ensure the safety and surety of the nuclear stockpile into the future. In addition to national security research, the LANSCE User Facility has a vibrant research program in fundamental science, providing the scientific community with intense sources of neutrons and protons to perform experiments supporting civilian research and the production of medical and research isotopes.

In low-dimensional and nanostructured materials, the optical response is dominated by correlated electron-hole pairs---or excitons---bound together by the Coulomb interaction. By now, it is well-established that these large excitonic effects in low dimensions are a combined consequence of quantum confinement and inhomogeneous screening. However, many challenges remain in understanding nonlinear and dynamical processes, especially when it comes to correlating complex experimental signatures with underlying physical phenomena using quantitatively predictive theories.

An increasingly large number of van der Waals materials have been found to be direct-bandgap semiconductors that feature simultaneously strong optical responses, unusual material properties, and unprecedented flexibility to construct heterostructures and to integrate with diverse substrates. These features enable access to coherent light-matter interaction regimes that are challenging or even inaccessible in traditional materials.

Living tissues grow and organize through a continuous feedback loop between mechanics and biological decision-making. Here, I present a framework that treats each cell as a “computing unit,” making discrete decisions such as division, differentiation, or migration, based on local mechanical and chemical cues. Drawing inspiration from Hopfield networks, we model these decisions as threshold-based rules whose interactions produce emergent, tissue-scale mechanics. This perspective allows us to bridge the gap between the physics of soft matter and the logic of biological computation.

In the pursuit of a sustainable future, the last decade has seen a concerted effort in accelerating the discovery of materials for energy needs. In this talk I will focus on few 2D materials that provide the playground for understanding and manipulating their chemical and physical properties to harness novel functionalities. I will show how defects in single-layer hexagonal boron nitride (h-BN) transform the local electronic structure such that it captures and converts CO2 to value added products [1].