University of Wisconsin–Madison

Tag: IRG 1

(2026) Physics-Aware Generative AI for Metallic Glasses

Wisconsin MRSEC IRG 1 developed a new generative AI model for metallic glasses, called GlassVAE. The model is based on ideas similar to ChatGPT, but adapted to describe the complex atomic structures of glasses. It is designed to recognize meaningful changes in atomic structure, rather than being affected by how atoms are positioned or labeled in traditional coordinate descriptions. Unlike many general-purpose AI models that often generate unrealistic atomic arrangements, GlassVAE includes “physics guardrails” based on structural features and energy, ensuring that the generated structures remain physically sensible.

(2026) Physical Aging of Organic Semiconductor Glasses

Wisconsin MRSEC IRG 1 researchers used three experimental techniques to characterize physical aging in organic semiconductor glasses. They showed that producing the glasses by physical vapor deposition can reduce the impact of aging by at least a factor of 10. In addition, they showed that routine laboratory measurements on bulk samples can provide reliable predictions for physical aging in the very thin films used in OLED displays. These results are relevant for OLED displays as glass stability is key to maintaining high efficiency, and they advance our MRSEC IRG goal to control mobility to create glasses with desirable properties.

(2025) Geometrically Accurate Coarse-Graining with AniSOAP

Wisconsin MRSEC researchers have developed a coarse-graining technique called AniSOAP (for anisotropic smooth overlap of atomic potentials) that gives the beads shapes that reflect the shape of the molecules they represent. This simple idea – carefully implemented to be mathematically rigorous and account for how molecules typically interact – can used for high-accuracy coarse grained simulations or to understand materials behavior that depends on molecular shape or orientation. AniSOAP is also particularly useful for machine learning analysis of molecular behavior using simple, physically-interpretable algorithms, producing new insight for researchers.

(2025) A Nanoscale View of Molecule Alignment in an Organic Semiconductor

Wisconsin MRSEC researchers have developed a new way to see how molecules fit together with an electron microscope. They used the method to see how molecules rearrange when an organic semiconductor is heated. A modest change in temperature creates significantly improved molecular alignment. The improved alignment is reflected in both larger aligned regions and straighter lines of molecules inside each region.

(2024) Control of Glass Structure and Properties with Soft Substrates

Physical vapor deposition (PVD) canproduce glassthinfilmswith preferred orientation to the molecules andhigherdensitythan ordinaryliquid-quenchedglass bytakingadvantageofthefastmovementoforiented moleculesonthe glasssurface.
Research supported by Wisconsin MRSEC have found a new way to control the structure and properties of these films by growing them on soft substrates. PVD on a soft substrate can produce glass thin films that are much more dense and stable than those deposited on rigid substrates. A film deposited on a soft substrate in 2 hours is equivalent to a film deposited extremely slowly on rigid substrates over ~3000 years.

(2024) Biaxially-aligned Glasses of Organic Semiconductors

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MRSEC Graduate Student Gives Two Presentations at IMC20

Shuoyuan Huang, a graduate student in Paul Voyles’ lab, recently attended the 20th International Microscopy Congress in Busan, Korea.  While there, he presented two talks: “Momentum-Resolved Electron Correlation Microscopy Reveals Structure Dependent Dynamics in Metallic Supercooled Liquids” and “High-speed, Low-dose 4D STEM of Orientation Domains in an Anisotropic Molecular Glass.”

(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.