In January, Qiyuan Chen presented MRSEC-supported research at the 40th AAAI Conference on Artificial Intelligence (AAAI-26) in Singapore. With a highly selective acceptance rate of 17.6% this year, AAAI remains one of the premier international venues for peer-reviewed research in artificial intelligence (AI). Chen’s presentation introduced a new physics-aware generative AI framework designed to decode the complex structure of disordered materials.
Generative AI Meets Disordered Materials
Generative AI has rapidly gained attention in materials science. However, disordered materials pose unique challenges. Unlike crystalline materials, disordered materials with the same chemical composition can exhibit an astronomically large number of possible atomic structures. While these structures share common structural motifs, they also contain subtle variations that can lead to different behaviors and properties.
Standard generative models, which typically function as “black boxes,” often produce mathematically plausible atomic structures that are nevertheless physically unrealistic. For example, generated structures may be energetically unstable or fail to reproduce the key structural motifs and signatures observed in real disordered materials.
Wisconsin Materials Research Science and Engineering Center
The NSF-sponsored Wisconsin Materials Research Science and Engineering Center brings together teams of researchers from diverse disciplinary backgrounds to tackle grand challenges in the materials science of liquids and glasses and non-equilibrium magnetism.