University of Toledo
Physics-informed AI for Polymer and Biomacromolecule Modeling and Design
The Shi Group has access to NVIDIA H100 GPU computing resources at highly affordable rates through the Ohio Supercomputer Center (OSC). We have also received a computing allocation through the NSF-funded ACCESS program to support our research. These resources support our AI method development, model training, and computational studies of polymers and biomacromolecules. UToledo's AI Institute is expected to provide additional local high-performance computing resources in the near future.
As a critical component of the BigSMILES-centered ecosystem, BigFingerprint converts BigSMILES into numerical fingerprints for AI/ML, combining local chemical environments (p-ECFP) with global polymer architecture (p-TFP). [Preprint] [Code]
Prof. Jiale Shi gave a talk titled “AI for Macromolecular Engineering: Interpretable Similarity Learning and Sequence-Aware Prediction” as part of the HowU Connect ChEminar Series, hosted by the Department of Chemical Engineering at Howard University. [Flyer PDF]
Prof. Jiale Shi gave a talk titled “Measuring Differences in Protein Allosteric Graphs via Molecular Dynamics Simulations, Graph Theory, and AI” at the 2nd SCBA-Ohio Chapter Annual Scientific Symposium.
Prof. Jiale Shi was scheduled to give a talk at UToledo AI Lightning Talks, hosted by the Department of Electrical Engineering and Computer Science and the Office of Research and Sponsored Programs.
Prof. Jiale Shi officially joined the University of Toledo Department of Chemical Engineering as a tenure-track assistant professor.
Jiale Shi, Zhongyi Wan, Renjie Zhu, Qiang Cui.
2026, 22, 12, 6180–6195.
Jiale Shi, Runzhong Wang, Nathan J. Rebello, Jiarui Lu, Debra J. Audus, Bradley D. Olsen.
2026, 59, 4, 1885–1900.
Led by Jiale Shi in the Department of Chemical Engineering at the University of Toledo, our fully computational lab develops physics-informed AI for polymers and biomacromolecules.
Meet the team