Education

• Ph.D., University of Wisconsin, 2023
• B.S., University of California - San Diego, 2018

Research

Our group focuses on the computational design of soft materials for energy and sustainability. Soft and polymeric materials play critical roles in technologies such as plastic upcycling, energy storage, separations, and advanced manufacturing, yet their performance depends on complex couplings among molecular chemistry, topology, processing, and macroscopic behavior. Our research aims to understand and navigate this multiscale design space by developing predictive models that connect molecular-scale behavior to material function and product-level performance.

Our approach integrates molecular modeling and simulation, theory, and machine learning into physics-informed design workflows. We emphasize data-efficient and interpretable models, uncertainty-aware prediction and closed-loop discovery strategies. These strategies guide materials selection and optimization while explicitly accounting for practical constraints such as synthesizability, stability, and processability.

Selected Publications

1. Jiang, M. A. Webb, “Generative Active Learning across Polymer Architectures and Solvophobicities for Targeted Rheological Behavior”, npj Computational Materials, 12, 28 (2026).
2. Jiang, M. A. Webb, “Physics-Guided Neural Networks for Transferable Property Prediction in Architecturally Diverse Copolymers”, Macromolecules, 58, 4971–4984 (2025).
3. Jiang, A. B. Dieng, M. A. Webb, “Property-Guided Generation of Complex Polymer Topologies Using Variational Autoencoders”, npj Computational Materials, 10, 139 (2024).
4. Jiang, S. Qin, R. C. Van Lehn, P. Balaprakash, V. M. Zavala, “Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search”, Digital Discovery, 3, 1534–1553 (2024).
5. Jiang, V. M. Zavala, “Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applications”, AIChE Journal, 67, e17282 (2021). DOI: 10.1002/aic.17282.