PhD in AI-Driven Soft Materials Design for Energy and Circular Economy
Explore AI-driven approaches combining molecular simulation and generative deep learning to design next-generation soft materials. Develop sustainable polymers and electrolytes with enhanced stability and scalability targeting energy and waste reduction.
AI-generated overview
Project Description
Project Overview
This project explores the design of soft materials leveraging AI-driven computational methods for applications in energy and circular economy challenges. It targets plastic upcycling, circular polymers, polymer electrolytes, ion-conducting membranes, and multiscale modeling of soft materials in complex fluids and processing.
What You Will Do
You will develop and integrate molecular simulations, machine learning approaches—including generative AI and geometric/topological deep learning—and theoretical modeling to guide the design of materials that are high-performing, synthesizable, stable, and scalable. The research will require combining computational chemistry, materials science, and AI techniques.
Expected Outcomes
The project aims to produce novel AI methodologies that enable accelerated discovery and design of sustainable soft materials. Results will contribute to efficient plastic recycling, novel polymer electrolytes for energy devices, and enhanced understanding of soft material behavior at multiple scales.
Why This Matters
Addressing urgent environmental issues, this research pushes the frontier of materials science to enable sustainable polymer solutions that reduce plastic waste and enhance energy technologies. Advancing AI in materials design also supports scalable production of eco-friendly materials critical for a circular economy.
Entry Requirements
How to Apply
Eligibility
Supervisor Profile
Dr. Shengli (Bruce) Jiang is an Assistant Professor at the University of South Carolina in Chemical Engineering, specializing in AI-driven computational design of soft materials. His research integrates molecular simulation, machine learning, and theory to develop sustainable materials for energy and environmental applications. With a growing profile, he advances intersections of materials science and AI to address critical circular economy challenges.