Shengli (Bruce) Jiang
Postdoctoral Associate | Lecturer
Department of Chemical and Biological Engineering
Princeton University
Email: sj0161@princeton.edu
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Shengli (Bruce) Jiang is a postdoctoral associate at Princeton University (advisor: Michael A. Webb), where he integrates molecular simulation and machine learning to design polymers with tailored properties. He received his Ph.D. in Chemical Engineering at the University of Wisconsin-Madison (advisor: Victor M. Zavala), focusing on new data representation methods for applications such as liquid crystal contaminant sensor design, mixed plastic waste classification, and chemical process monitoring.
He interned at Dow Inc. (2022, supervisors: Ivan Castillo and Zhenyu Wang), working on large language models and electricity price forecasting, and at Argonne National Laboratory (2020, supervisor: Prasanna Balaprakash), where he contributed to neural architecture search and uncertainty quantification. He was also an undergraduate researcher at the University of California, San Diego (advisor: Zheng Chen), where he worked on the design of organic electrodes for lithium-ion batteries.
He is passionate about teaching, having instructed a machine learning course at Princeton and mentored eleven junior scholars. His research lies at the interface of computational materials science and process systems engineering, with a particular emphasis on data science. His interests span data-centric modeling, design, and optimization of complex soft materials.
News
News
Our work with Satyen Dhamankar is featured on the cover of Molecular Systems Design & Engineering! The paper is available here.
Phase separation in multicomponent mixtures is of significant interest in both fundamental research and technology. In this paper, we develop a machine-learning workflow that predicts phase coexistence with improved accuracy using physics-informed constraints.
Selected Publications
Selected Publications
S. Jiang and M. A. Webb. Physics-guided neural networks for transferable property prediction in architecturally diverse copolymers. Macromolecules, 58(10):4971–4984, 2025.
S. Dhamankar*, S. Jiang*, and M. A. Webb. Accelerating multicomponent phase-coexistence calculations with physics-informed neural networks. Molecular Systems Design & Engineering, 10(2):89–101, 2025. *Equal contribution
C. Lucky, S. Jiang, C.-R. Shih, V. M. Zavala, and M. Schreier. Understanding the interplay between electro-catalytic C(sp3)–C(sp3) fragmentation and oxygenation reactions. Nature Catalysis, 7(1):1–11, 2024.
S. Jiang, A. B. Dieng, and M. A. Webb. Property-guided generation of complex polymer topologies using variational autoencoders. npj Computational Materials, 10(1):139, 2024.
S. Jiang, S. Qin, R. C. Van Lehn, P. Balaprakash, and V. M. Zavala. Uncertainty quantification for molecular property predictions with graph neural architecture search. Digital Discovery, 3(8):1534–1553, 2024.
S. Jiang, N. Bao, A. D. Smith, S. Byndoor, M. Mavrikakis, R. C. Van Lehn, N. L. Abbott and V. M. Zavala. Scalable extraction of information from spatio-temporal patterns of chemoresponsive liquid crystals using topological data analysis. The Journal of Physical Chemistry C, 127(32):16081–16098, 2023.
N. Bao*, S. Jiang*, A. D. Smith, J. J. Schauer, M. Mavrikakis, R. C. Van Lehn, V. M. Zavala, and N. L. Abbott. Sensing gas mixtures by analyzing the spatiotemporal optical responses of liquid crystals using 3D convolutional neural networks. ACS sensors, 7(9):2545–2555, 2022. *Equal contribution.
S. Qin, S. Jiang, J. Li, P. Balaprakash, R. C. Van Lehn, and V. M. Zavala. Capturing molecular interactions in graph neural networks: a case study in multi-component phase equilibrium. Digital Discovery, 2(1):138–151, 2023.
S. Zinchik*, S. Jiang*, S. Friis, F. Long, L. Høgstedt, V. M. Zavala, and E. Bar-Ziv. Accurate characterization of mixed plastic waste using machine learning and fast infrared spectroscopy. ACS Sustainable Chemistry & Engineering, 9(42):14143–14151, 2021. *Equal contribution.
S. Jiang, J. Noh, C. Park, A. D. Smith, N. L. Abbott, and V. M. Zavala. Endotoxin detection using liquid crystal droplets and machine learning. Analyst, 146(4):1224–1233, 2021.
A. K. Chew, S. Jiang, W. Zhang, V. M. Zavala, and R. C. Van Lehn. Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks. Chemical Science, 2020, 11(46):12464–12476, 2020.
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