Stability in Glasses: New Materials and New Insights

IRG 1 Leaders:

paul voyles

Paul Voyles
223 MSE Building
1509 University Avenue
Madison, WI 53706

Mark Ediger

Mark Ediger
7303A Chemistry Building
1101 University Avenue
Madison, WI 53706

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Glasses are ubiquitous across materials types and technological applications but their structure – property – processing relationships and underlying fundamental physics remain poorly understood. IRG 1 uses cross-fertilization of ideas and techniques from organic and inorganic glasses to address fundamental problems in glass science through the lens of stability. Glasses of the same composition can be created in states of widely varying thermodynamic and kinetic stability. The IRG seeks to use these materials to develop fundamental stability-structure-property relationships for glasses. Efforts include establishing control over stability in organic and inorganic glasses; understanding the structures associated with varying states of stability ; discovering the molecular nature of polyamorphism – the existence of two stable liquid states of the same substance; and determining the relationship between the structure and dynamics of liquids as they cool into the glassy state. The IRG integrates theory, simulations, and experiments to expand the range of ultrastable glassy materials and to enable new applications in areas as diverse as hard coatings and quantum information.

IRG 1 Highlights

  • (2022) Predicting Surface Diffusion on Molecular Glasses

    Molecules near to the surface of a glass move much faster than molecules on the inside – up to a billion times faster. Making glasses often involves adding new molecules from the surface, so high surface mobility is crucial for making materials for cell phone displays, organic solar cells, and drug delivery.

  • (2022) Low Temperature Properties of Glass and its Connection to Glass Stability

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  • (2021) New Insights into Surface Diffusion on Glasses

    Understanding how atoms move is fundamental to making and using materials. Atoms on the surface of some glasses move at nearly the same rate as atoms on the inside. But for other glasses, surfaces atoms move a million times faster. Researchers in the Wisconsin MRSEC IRG 1 have combined experiments, simulations, and data-centric methods to understand why some surfaces are so much faster than others. They found that atoms in glasses move by breaking out of a “cage” of nearby atoms. On the surface, that cage can be weaker than inside the glass, allowing for faster motion. They also discovered a relationship that predicts surface motion from more accessible data about bulk motion. Their results unify behavior for glasses of organic molecules, metals, and oxides and make creating glasses for applications like light-emitting diodes, quantum computers, and hard coatings easier.

  • (2021) Use Machine Learning to Link Atomic Structure with Glass Properties and Behaviors

    Glasses have disordered arrangements of atoms without the repeating patterns that crystals have. However, there are small-scale patterns of atoms that touch each other that strongly affect the energy of the glass, how the atoms move when they get hot, and other properties like strength and response to an electric field. Unfortunately, there are many possible patterns and many slight variations of each one, so studying them is like sorting the grains of sand on a beach by size and color by hand–it’s an impossible task. Wisconsin MRSEC IRG 1 uses machine learning to sort the sand. They have developed algorithms to find small-scale atomic patterns in large simulations of glasses and link them to the glass’ energy. Ongoing studies have connected patterns to atomic motions, which provides a path to simulations of glasses over long times and low temperatures that are currently impossible.

  • Large Dataset on the Relationship Between Structure and Stability in Glass Wins Open Science Award for Yu

    The generation and sharing of a large dataset created as part of his study has won Zheng Yu the 2021 Wisconsin MRSEC Excellence in Open Science Prize. A graduate student in Dr. Bu Wang’s lab at the Grainger Institute for Engineering, Yu generated the data as part of his work investigating the relationship between structure and stability in specialized glasses using computer simulations and machine learning.

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