MSE Department Seminar

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.

Voltage imaging provides unparalleled spatial and temporal resolution of the brain’s electrical signaling at the cellular and circuit levels. A longstanding challenge has been to develop genetically encoded voltage sensors to track membrane voltage from multiple neurons in behaving animals. However, brightness and signal to noise ratio have limited the utility of existing voltage sensors, especially in vivo.

Dynamic materials comprising of soft solids and structured liquids that can adapt to surrounding environment are poised to be key components of future technologies. They offer the unique opportunity couple changes in their mechanical confirmation to physical properties and function via their micro and nano scale architecture. However, soft and fluid materials are seldom used for optical engineering.

Solid-state batteries pose new challenges to the battery design due to the unique solid-solid interfaces at battery cathode and anode. However, these interfaces, upon critical understanding and design, also form a special opportunity to unlock advanced battery performances. We design solid state batteries based on our unique mechanical constriction principle and the constrained ensemble computational platform, for a stable cycling toward performance relevant conditions.

Programmable shape-shifting materials can take different physical forms to achieve multifunctionality in a dynamic and controllable manner. We take geometry from nano- to macroscales by (re)programming anisotropy in liquid crystal elastomers (LCEs) and their nanocomposites in the forms of films, fibers, and droplets. Through inverse engineering, that is pre-programming inhomogeneous local deformations in LCEs, we show shape morphing into arbitrary 3D shapes. By incorporating 1D and 2D nanomaterials (e.g.

We address the problem of learning of classical Markov Random Fields that are widely used in material science, statistical physics, and computer science to represent structured Gibbs distributions. We introduce a new computationally efficient Interaction Screening method for learning discrete and continuous Gibbs distributions for which maximum likelihood approaches are intractable. The algorithm recovers the structure and parameters of the Hamiltonians with multi-body interactions specified in an arbitrary basis.