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Shuwen Yue
Shuwen Yue
Graduate Field Affiliations
Chemical Engineering
Materials Science and Engineering
Mechanical Engineering

Biography

Shuwen Yue joined the Smith School of Chemical and Biomolecular Engineering in the Summer of 2023. She received a B.S. dual degree in Chemical Engineering and Chemistry from the University of Alabama in 2016. Shuwen then received her Ph.D. in Chemical and Biological Engineering from Princeton University in 2021, where she worked on developing machine learning potentials for molecular fluids with Athanassios Z. Panagiotopoulos. She then conducted postdoctoral research in the Department of Chemical Engineering at MIT on machine learning for materials with Heather J. Kulik.

Research Interests

We study how the chemistry of individual molecules – their structure, charges, and interactions – gives rise to the large-scale behaviors observed in liquids, mixtures, and materials. By understanding and controlling how molecules work together in the condensed phase, we can design systems with tailored properties, such as how fast ions move through a battery or how efficiently a reaction occurs on a catalyst.

We use physics-based and data-driven approaches to guide the bottom-up design of electrolytes, interfaces, and catalytic environments, targeting control over phase behavior, chemical reactivity, and transport from molecular principles. Our group also develops machine learning models and methods tailored to chemical systems, with a focus on integrating physical insight to improve robustness, efficiency, and interpretability. Our work is grounded in statistical mechanics, molecular simulations, and AI/ML, which we integrate to advance catalysis, energy storage, and sustainable chemistry.

  • Computational Science and Engineering
  • Artificial Intelligence
  • Statistical Mechanics and Molecular Simulation
  • Statistics and Machine Learning
  • Complex Fluids and Polymers
  • Energy and the Environment

Select Publications

  • Yue, S.*, Muniz, M. C.*, Andrade, M. F. C., Zhang, L., Car, R., and Panagiotopoulos, A. Z. When do short-range atomistic machine-learning models fall short? Journal of Chemical Physics. (2021). 154, 034111.

  • Zhang, C., Yue, S., Panagiotopoulos, A. Z., Klein, M. L., and Wu, X. Dissolving salt is not equivalent to applying a pressure on water. Nature Communications. (2022). 13, 822.

  • Zhang, C., Yue, S., Panagiotopoulos, A. Z., Klein, M. L., and Wu, X. Why dissolving salt in water decreases its dielectric permittivity. Physical Review Letters. (2023). 2304893.

  • Yue, S., Oh, C., Nandy, A., Terrones, G. G., and Kulik, H. J. Effect of MOF linker rotation and functionalization on methane uptake and diffusion. Molecular Systems Design & Engineering. (2023). 8, 527-537.

  • Yue, S. and Panagiotopoulos, A. Z. Dynamic Properties of Aqueous Electrolyte Solutions from Nonpolarisable, Polarisable, and Scaled-Charge Models. Molecular Physics. (2019). 117 (23-24), pp 3538-3549.

Select Awards and Honors

  • Scialog Fellow, Sustainable Minerals, Metals, and Materials (SM3) 2024
  • Best Poster Award, Foundations of Molecular Modeling and Simulation (FOMMS) 2022
  • WCC Merck Award, The American Chemical Society 2020
  • Best Talk in Computational Modeling, Department of Chemical and Biological Engineering, Princeton University 2019
  • Mary and Randall Hack ‘69 Graduate Award, Princeton University 2019
  • Francis Robbins Upton Fellowship, Princeton University 2016
  • Tau Beta Pi Fellowship 2016
  • Tau Beta Pi Scholarship 2015

Education

  • B.S., Chemical Engineering & Chemistry, The University of Alabama 2016
  • Ph.D., Chemical & Biological Engineering, Princeton University 2021
  • Postdoctoral Associate, Massachusetts Institute of Technology 2023