Qian-Ze Rosella Zhu, Pappalardo Fellow

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Area of Physics

Biophysics/Soft Condensed Matter

Research Interests

In What is Life?, Schrödinger challenged physicists to treat biology as a problem of physics: how can life achieve order, precision, and adaptability amid thermal noise and constant energy flux? A central challenge in biophysics is to explain how complex functions emerge from local stochastic rules and to elevate these explanations into predictive models that reveal universal laws. Qian-Ze Zhu’s research uses statistical physics, differentiable simulation, and machine learning to connect structure, dynamics, and computation across scales, and to derive limits and design principles testable in experiments.

During her PhD, Qian-Ze developed differentiable molecular dynamics simulations for inverse design of self-assembling systems and used hybrid physics-ML models to design energy-driven molecular proofreading mechanisms. Qian-Ze also extended these ideas to computation, deriving fundamental scaling limits for molecular computing and designing nonequilibrium pathways towards more scalable computation. In parallel, she is working on end-to-end differentiable optimization of stochastic (Gillespie) simulations, with applications to maximizing information flow in genetic circuits.

At MIT, Qian-Ze is excited to study nonequilibrium learning and decision-making in the adaptive immune system and eco-evolutionary feedback in active-matter systems. By combining mechanistic models with differentiable optimization, she aims to quantify how energy use, noise, and local feedback shape function, and to extract experimentally testable design principles.

Biographical Sketch

Qian-Ze grew up in Wuhan, China. She received B.S. in Physics from Peking University in 2021, where she worked on quantum optics and optomechanics with Prof. Yun-Feng Xiao. Qian-Ze then began her Ph.D. in Applied Physics at Harvard. There, she shifted her interests to soft matter and biophysics and joined Prof. Michael P. Brenner’s group in 2022, where she develops physics-based theory and differentiable simulations to study function and computation in complex systems out of equilibrium.

Outside of research, Qian-Ze enjoys traveling, playing tennis and piano and (occasionally) contemplating the meaning of life. No good answers yet.

Selected Publications

• Q.-Z. Zhu, C. X. Du, E. M. King, and M. P. Brenner, Proofreading mechanism for colloidal self-assembly, Phys. Rev. Research 6, L042057 (Editor’s Suggestion), 2024.
• E. Crawley*, Q.-Z. Zhu*, M. P. Brenner, Fundamental Scaling Constraints for Equilibrium Molecular Computing, arXiv:2509.20526, 2025.
• F. Mottes, Q.-Z. Zhu, M. P. Brenner, Gradient-based optimization of exact stochastic kinetic models, arXiv:2601.14183, 2026.
• E. M. King*, C. X. Du*, Q.-Z. Zhu, S. S. Schoenholz, and M. P. Brenner, Programmable patchy particles for materials design, Proc. Natl. Acad. Sci. 121 (27), 2023.
• Z. Liang*, M. X. Lim*, Q.-Z. Zhu, F. Mottes, J. Kim, L. Guttieres, C. Smart, T. Pearson, C. X. Du, M. P. Brenner, P. L. McEuen, and I. Cohen, Magnetic Decoupling as a Proofreading Strategy for High-Yield, Time-Efficient Microscale Self-Assembly, Proc. Natl. Acad. Sci. 122 (35), 2025.