MSE Department Seminar

The overarching theme of our research program is “Biomimetic Macromolecules at the Materials-Biology Interface”, which encompasses nature-inspired molecular-level design and synthesis of polymeric materials toward unprecedented control of structure and biological activity. The central aim of our work is to understand how tuning the chemical structure and physical properties of polymer chain molecules can influence their interactions with living systems such as bacterial or mammalian cells.

As a textbook example of strongly correlated electron material, vanadium dioxide (VO2) features a metal-insulator transition (MIT) when temperature drops below 67oC. The physics of the MIT has challenged physicists for decades, while the potential of the MIT has inspired researchers for a wide range of applications. In this lecture, I will talk about two of our recent works. On the fundamental side, we have revealed novel charge dynamics of VO2 by investigating its electronic thermal conductivity across the MIT [Science, 355, 371(2017)].

The temporal evolution of ordered γ´(L12)-precipitates and the compositional trajectories during phase-separation of the γ(face-centered-cubic (f.c.c.))- and γ′(L12)-phases are studied in a Ni–0.10Al-0.085Cr-0.02Re (mole-fraction) superalloy, utilizing atom-probe tomography, transmission electron microscopy, and the Philippe-Voorhees (PV) coarsening model. As the γ′(L12)-precipitates grow, the excesses of Ni, Cr and Re, and depletion of Al in the γ(f.c.c.)-matrix develop as a result of diffusional fluxes crossing γ(f.c.c.)/γ′(L12) heterophase interfaces.

Oxygen vacancies are known to be critical to the behavior of complex oxide heterostructures. Here we describe two studies exploiting in situ synchrotron X-ray techniques.

Organic semiconductors have been traditionally developed for making low-cost and flexible transistors, solar cells and light-emitting diodes. In the last few years, emerging applications in health case and bioelectronics have been proposed. An interesting class of materials in this application area takes advantage of mixed ionic and electronic conduction in certain semiconducting polymers. In particular, mixed conductors can be used to emulate the compute-in-memory operation of the brain that is the key to its energy efficiency.

Resistance in nanoscale wires is increasingly the main bottleneck in the performance of semiconductor computing devices. With reducing dimensions, scattering of electrons at surfaces, interfaces and grain boundaries increases rapidly and causes a sharp increase of resistivity of conventional metals at the nano scale compared to bulk. We use first-principles calculations of ballistic electron transport and electron-phonon scattering to explore several complementary strategies to design materials for future nanoscale interconnects.

Twisted bilayer materials, and in particular, twisted bilayer graphene, are created by slightly rotating the two individual crystal networks in a 2D material with respect to each other. For small twist angles, the material undergoes a self-organized lattice reconstruction, leading to the formation of a periodically repeated domain. The resulting superlattice modulates the vibrational and electronic structures within the material, leading to changes in the behavior of electron–phonon coupling and to the observation of strong correlations and superconductivity.

A sustainable future is axiomatically a carbon-free electric future. Emerging technologies that will usher in this new economy necessarily include electrochemical innovations in energy storage and in steelmaking. Electricity storage is critical to widespread deployment of carbon-free but intermittent renewables, solar and wind, while offering huge benefits to today’s grid: improving security and reducing price volatility. Invented at MIT, the liquid metal battery provides colossal power capability on demand and long service lifetime at very low cost.

Materials form the backbone of every society. This is evidenced from the naming of human civilizations as Stone Age, Bronze Age, Iron Age, to name a few. Traditional materials discovery relies on trial and error approaches thereby leading to a design to deploy period of 20- 30 years. To address this challenge, in this talk, we will discuss the application of artificial intelligence (AI) and machine learning (ML) in accelerating materials modeling and discovery.