Jiang Group | Computational Materials and Product Design

Shengli Jiang, PhD

Assistant Professor

Department of Chemical Engineering

University of South Carolina

Office 2C19 Swearingen

shengli@mailbox.sc.edu

Our research combines physics-informed machine learning with molecular simulation to design soft materials and chemical products for energy and sustainability.

Soft-material behavior emerges from interactions across multiple length and time scales. We combine simulation, theory, and data to understand structure-property relationships and develop predictive models grounded in physical principles.

Our Approach

Machine Learning Methods We develop physics-informed neural networks, geometric and topological deep learning methods, and generative models.

Molecular Simulation We use molecular simulation to relate molecular structure and interactions to bulk properties.

Materials and Product Design Predictive models guide the search for materials that can be synthesized, processed, and scaled.

Join Us

We are recruiting two PhD students for Spring 2027 and a postdoctoral researcher to work on AI‑driven soft materials design.

View openings & apply →

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.
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!