Research

Exploring nonequilibrium self-organization and molecular computing through statistical physics, differentiable simulation, and machine learning.

My research focuses on developing theoretical and computational methods for understanding complex stochastic systems, bridging statistical physics, differentiable simulation, and machine learning.
For a complete list of publications, please see my Google Scholar profile

Nonequilibrium Self-Organization

Exploring how nonequilibrium driving and energy dissipation can steer robust self-assembly through mechanisms such as proofreading, error correction, and state change, enabling high-yield assembly beyond equilibrium limits.

Selected Publications

Proofreading mechanism for colloidal self-assembly
QZ Zhu, CX Du, EM King, MP Brenner
Physical Review Research 6 (4), L042057, 2024 (Editor's Suggestion)· Paper

Magnetic decoupling as a proofreading strategy for high-yield, time-efficient microscale self-assembly
Z Liang*, MX Lim*, QZ Zhu, F Mottes, JZ Kim, L Guttieres, C Smart, ...
PNAS 122 (35), 2025 · Paper

Molecular Computing

Studying how computation can be embedded in molecular dynamics, where tailored interactions and nonequilibrium kinetics guide physical and biological systems toward solutions of hard combinatorial problems. I focus on programmable self-assembly, error correction, and the thermodynamic constraints that shape how information is processed in physical matter.

Selected Publications

Fundamental Scaling Constraints for Equilibrium Molecular Computing
E Crawley*, QZ Zhu*, MP Brenner
arXiv preprint, 2025 · Paper

Differentiable Simulation

Developing end-to-end differentiable molecular dynamics and stochastic simulations to enable inverse design and optimization of stochastic physical and biological systems, especially far from equilibrium.

Selected Publications

Programming patchy particles for materials assembly design
EM King*, CX Du*, QZ Zhu, SS Schoenholz, MP Brenner
PNAS 121 (27), e2311891121, 2024 · Paper

Inferring interaction potentials from stochastic particle trajectories
EM King*, MC Engel*, C Martin, AM Sunol, QZ Zhu, SS Schoenholz, ...
Physical Review Research 7 (2), 023075, 2025 · Paper

Computational Biology

Building on these computational and theoretical tools, I aim to develop interpretable, physics-grounded models of biological systems directly from experimental data and to investigate the optimality principles governing living systems. I am particularly interested in how noise, feedback, and network topology shape robustness, adaptation, and decision-making, from gene regulatory networks to neural systems.

Publications to come...

AI for Science

Developing AI systems that leverage large language models and search to automate scientific modeling, accelerate discovery, and build generalizable computational tools for physics and biology.

Selected Publications

An AI system to help scientists write expert-level empirical software
E Aygün, A Belyaeva, G Comanici, M Coram, H Cui, J Garrison, RJA Kast, ...
arXiv preprint, 2025 · Paper

Generalizing PDE Emulation with Equation-Aware Neural Operators
QZ Zhu, P Raccuglia, MP Brenner
NeurIPS Workshop: ML4PS, 2025 · Paper

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