Meta-Stable Materials Lab
Decoding Materials for a Sustainable Future
Rational-design of Meta-stability and Evolution of Advanced Materials
our lab we target meta-stable materials that often include high degrees
of (static or dynamic) disorder, such as glasses, high-entropy alloys, soft-semiconductors (e.g., halide perovskites), 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 structure and functionality.
Often, these systems are dynamically changing during their
functionalization and exist out of a thermodynamic equilibrium. Therefore, to understand them
and rationally guide their development, one must follow and control their structural
We develop experimental and analytical data-driven tools to learn about the strucutral evolution from the earliest states of disorder towards ordering, try to understand what (de)stabilize meta-stable materials, and how one can control their evolution.
What do we do?
- We develop state-of-the-art high-resolution tools, which implement data-driven experimental approaches, for an in-situ/operando strucutral evolution investigation.
- We develop and implement machine-learning and image-processing algorithms for disentangling phase-complexity and target desired meta-stable phases.
We develop active control approaches of functionalizing meta-stable
and regenerative materials, meaning materials that undergo cyclic degradation/ self-healing
We are Hiring! (For details, see ' Open Positions '. )
- We are looking for graduate students to work on the order evolution of meta-stable material systems using a combination of advance characterization techniques and Machine-Learning