Thrust 1
Developing Conditional Generative Model for Complex Polymer Design
We develop generative models for complex polymers that can reason over chemistry, architecture, topology, and target material properties.
The Shi Group is a Chemical Engineering research group developing physics-informed AI methods, including generative models, language models, AI for science foundation models, and graph learning, to understand, design, and engineer complex polymers, soft materials, macromolecules, biomacromolecular systems, and molecular interfaces. We are particularly interested in developing physics-informed AI models into applications of polymers and biomacromolecules in healthcare, sustainability, energy, and gas separation.
Thrust 2
Developing Environment-aware AI Models to Improve Property and Response Prediction for Complex Polymers
We build predictive AI models that connect polymer structure, physical state, external conditions, and material response across polymer families.
Thrust 3
Developing AI Models to Accelerate and Interpret Complex Polymer Simulation
We integrate molecular simulation, field-based modeling, and machine learning to accelerate sampling, connect scales, and interpret polymer structure-property relationships.
Thrust 4
Developing AI Models to Predict Protein/RNA Allosteric Regulation and Ensemble Dynamics
We develop physics-informed AI methods to predict biomacromolecular regulation, conformational ensembles, and dynamic molecular function.