Meta-stable and Evolving Materials

In our lab we target meta-stable materials that often include high degrees of disorder, such as glasses, high-entropy alloys, and phase-change materials. While these materials are candidates for neuromorphic computation applications, and energy recycling, harvesting and storage applications, their high degree of disorder require non-traditional approaches to create links between their strucutre and functionality. Often, these systems are dynamically changing during their functionalization or out of equilibrium. Therefore, to understand them and rationally guide their development, one must follow their structural evolution.

What do we do?
- Develop new tools that implement data-driven experimental approaches to identify, in-situ, strucutral evolution in high resolution.
- Develop and implement machine-learning and image-processing algorithms for disentangling complexity and target desired meta-stable phases.
- Develop active control approaches of functionalizing meta-stable materials and materials that undergo cyclic degradation/ self-healing processes.