This year marks the 27th anniversary of IBM’s CMOS/Cu BEOL [Back End of the (manufacturing) Line] on-chip interconnect technology reaching production, with the first high-end Cu CPU chips passing manufacturing qualification and being sampled to customers. This announcement shocked the industry, who were unaware that Cu had been taken beyond the early research phase.
Innovative materials are needed to tackle current major challenges in energy storage and generation. However, the design of new materials largely relies on experimental trial and error, limiting the number explored compounds relative to the entire space of possible compounds. In this presentation, I will discuss our approach to materials design, which integrates machine learning (ML) techniques with quantum mechanics-based computations.
Advances in optics and in the harnessing of light-matter interactions have led to corresponding advances in coherent optical excitation and monitoring of collective degrees of freedom including transverse and longitudinal acoustic modes, optical phonons, and magnons both within and far beyond their linear-response regimes.
Despite being an old material in optical and microwave technologies in its bulk form, thin-film lithium niobate (TFLN) has recently emerged as one of the most promising integrated photonic platforms owing to its strong electro-optic (EO) coefficient, quadratic optical nonlinearity, and broadband optical transparency ranging from 350 nm to 5 µm. In this talk, I will first overview the basic optical properties of LN, and how LN nanophotonics can grant us new regimes for studying ultrafast and nonlinear photonics.
Magnetic impurity doped monolayer transition metal dichalcogenides (TMDs) have been theoretically predicted to be above room temperature ferromagnets when doping/alloying levels are high (greater than 15% substitution of the transition metal). Due to their monolayer thickness, RT ferromagnetic TMDs could be useful in highly efficient memory devices as interface exchange and strain coupling scales as 1/t FM.
Traditionally materials design relies on empirical methods such as trial-and-error, high-throughput screening, or combinatory experimentation. In contrast, inverse materials design adopts a systematic and computational approach to identify and engineer materials suitable for specific purposes. In this talk, I will present case studies highlighting the power of molecular theory and machine learning for the inverse design of nanostructured materials for chemical separation, wet adhesion, and electrochemical energy storage.
Polaritons are quasiparticles of mixed optical-material nature that provide an opportunity for designing new properties in material systems. Coupling vibrational modes to optical cavities results in vibration-cavity polaritons that have been shown to modify chemical reaction rates and branching ratios. Our group has been investigating whether cavities alter transient properties such as relaxation of molecular vibrations, which might explain the modified chemistry.
The forthcoming era of energy systems, propulsion, space re-entry vehicles, and nuclear reactors points to structural materials that can withstand increasingly extreme thermal environments. Body- centered cubic (BCC) refractory alloys are attracting renewed attention owing to the nature of atomic bonding and internal plastic dissipation pathways, yet at ambient conditions, they tend to exhibit ceramic-like behavior characterized by low toughness and ductility.
When molecules are coupled to an optical cavity, new light-matter hybrid states, so- called polaritons, are formed due to quantum light-matter interactions. With the experimental demonstrations of modifying chemical reactivities by forming polaritons under strong light-matter interactions, theorists have been encouraged to develop new methods to simulate these systems and discover new strategies to tune and control reactions. In this talk, I will first introduce the concept of polariton and discuss its relevance in the recent molecular polariton and polariton chemistry investigations.
Gels are key materials in biological systems such as tissues and may control biocondensate formation and structure. To further understand the effects of elastic environments on biomacromolecular assembly, we have investigated phase behavior and radii of coacervate droplets in polyacrylamide (PAM) networks as a function of the gel modulus. Poly-L-lysine (PLL) and sodium hyaluronate (HA) coacervate phases were prepared in PAM gels with moduli varying from 0.035 – 9.0 kPa. The size of the coacervate droplets is reported from brightfield microscopy and confocal fluorescent microscopy.
