Jiang Group | Computational Materials and Product Design
Department of Chemical Engineering | Molinaroli College of Engineering and Computing | University of South Carolina
We study the physics of polymers and other soft materials for energy dominance and the circular economy. We also develop physics-informed machine learning methods, with particular interest in representation and learning from limited data.
Shengli Jiang, PhD
Assistant Professor
Department of Chemical Engineering
University of South Carolina
Office 2C19 Swearingen
Soft materials for energy dominance
We study how molecular interactions, chain motion, and material structure govern transport, mechanical response, and stability. Understanding these relationships helps explain the performance of soft materials in energy storage and conversion.
Soft materials for the circular economy
Polymer recovery and reuse depend on composition, compatibility, and changes in molecular structure. We study how mixtures, interfaces, and reprocessing affect material behavior and the properties retained through successive processing cycles.
Physics-informed machine learning
We investigate how physical knowledge enters surrogate and hybrid models, constraints, and priors. We study how these choices interact with the representation of a system and affect learning when data are scarce.
Join Us
We welcome undergraduate researchers and expect to recruit one PhD student, starting in Spring or Fall 2027.
news
| Aug 24, 2026 | New course launching this fall — ECHE 589: Machine Learning in Chemical Engineering is now open for enrollment. MWF 10:50–11:40 AM. Learn more. |
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| Jul 23, 2026 | Our paper, Range-Aware Bayesian Optimization for Discovering Diverse Designs within Target Property Windows, is now published in Digital Discovery. Read the paper. |
| Apr 12, 2026 | The Jiang Group is officially launching at the University of South Carolina!  |