Meta-Stable Materials Lab
Decoding Materials for a Sustainable Future
Mission
Rational-design of Meta-stability and Evolution of Advanced Materials
In
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
evolution.
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.
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We develop active control approaches of functionalizing meta-stable
and regenerative materials, meaning materials that undergo cyclic degradation/ self-healing
processes.
Projects
Data-Driven Structure Mapping
We develop data-driven infrastructure for studying the evolution of nano-structures in complex environments.
Functional Glasses
We study amorphous inorganic structures for advance applications, such as neuromorphic computation, energy recycling and storage.
Targeted Active Reaction Control
We develop methods for targeting desired products by stabilizing unique intermediates with a machine-learning based active reaction control.
Functional Regenerative Materials
We study pathways for stabilizing and integrating functional materials that degrade under normal operation conditions, but that also have self-healing properties, such as Halide-Perovskites.
Highlights
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