Meta-Stable Materials Lab

Decoding Materials for a Sustainable Future


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. 
  • We develop active control approaches of functionalizing meta-stable and regenerative materials, meaning materials that undergo cyclic degradation/ self-healing processes.


Data-Driven  Structure Mapping

We develop data-driven infrastructure for studying the evolution of nano-structures in complex environments.

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Functional Glasses 

We study amorphous inorganic structures for advance applications, such as neuromorphic computation, energy recycling and storage.

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Targeted Active Reaction Control

We develop methods for targeting desired products by stabilizing unique intermediates with a machine-learning based active reaction control.

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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.

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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
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