Shuwen Yue
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 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 and the DOE Center for Chemistry in Solutions and Interfaces. She then conducted postdoctoral research in the Department of Chemical Engineering at MIT on machine learning for materials with Heather J. Kulik and the DOE Center for Enhanced Nanofluidic Transport.
Research Interests
Water is arguably the most important molecule in the physical sciences. Its structure, dynamics, and interaction with solutes and surfaces control how energy is generated and stored, prevent pollutants from entering ecosystems, and deliver drugs efficiently inside living things. The Yue Research Group seeks to unravel the intricate relationship between the molecular features of solutes in water and the subsequent influence on macroscopic fluid and interfacial behavior.
We apply multi-scale modeling, machine learning, and statistical mechanics towards the design of novel electrolytes and materials for energy and sustainability technologies. By bridging molecular scale driving forces with macroscopic observables and properties, we tackle challenges in the design and development of energy storage devices and separations materials.
- Computational Science and Engineering
- Artificial Intelligence
- Statistical Mechanics and Molecular Simulation
- Statistics and Machine Learning
- Complex Fluids and Polymers
- Energy and the Environment
Selected 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.
Roh, H., Yue, S., Hu. H., Chen, K., Kulik, H. J., Gumyusenge, A. Leveraging Polymer Electrochromism for Organic Electrochemical Synaptic Devices. Advanced Functional Materials. (2023). 2304893.
Nandy, A., Yue, S., Oh, C., Duan, C., Terrones, G. G., Chung, Y. G., and Kulik, H. J. A database of ultrastable MOFs reassembled from stable fragments with machine learning models. Matter. (2023). 6, 5, 1585-1603.
Yue, S., Riera, M.*, Ghosh, R.*, Panagiotopoulos, A. Z., and Paesani, F. Transferability of data-driven, many-body models for CO2 simulations in the vapor and liquid phases. Journal of Chemical Physics. (2022). 156, 104530.
Selected Awards and Honors
Early Career Research Award, Foundations of Molecular Modeling and Simulation (FOMMS), 2022
Best Poster Award, FOMMS, 2022
WCC Merck Award (The American Chemical Society), 2020
Best Talk in Computational Modeling (Princeton CBE), 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