We are a research group in the Department of Physics, Engineering Physics, and Astronomy at Queen's University. We work on emergence, systems physics, soft matter, materials physics, statistical mechanics, and machine learning.
Group NewsHere is some recent news from our group.
A news archive can be found at this link.
2026/05/04
2026/05/01
Melanie Phillips is continuing her work this summer as an NSERC USRA student.
2026/05/01
Irina Babayan was awarded a Doctoral Canada Graduate Research Scholarship. Congratulations!
2026/04/23
Gabrielle Barsky-Giles completed her physics thesis. Gabrielle be heading to Harvard to do a PhD in Physics in September. Congratulations!
2026/04/23
Don Kim completed his physics thesis. Congratulations!
2026/04/23
Melanie Phillips completed her engineering physics thesis. Melanie was the co-recipient of the Dean's Scholar in Engineering Physics and will be heading to UC Berkeley to do a PhD in Physics in September. Congratulations!
2026/04/23
Jaime Glerum completed her engineering physics thesis. Congratulations!
2026/02/20
2025/12/05
2025/11/15
Dr. Hazhir Aliahmadi submitted his PhD dissertation. Congratulations!
2025/09/15
Don Kim is joining the group for 2025-2026 for his honours physics thesis. Welcome!
2025/09/15
Gabrielle Barsky-Giles is joining the group for 2025-2026 for her honours physics thesis. Welcome!
2025/09/15
Melanie Phillips is joining the group for 2025-2026 for her undergraduate engineering physics thesis. Welcome!
2025/09/15
Jaime Glerum is joining the group for 2025-2026 for her undergraduate engineering physics thesis. Welcome!
We focus on understanding, predicting, and controlling emergent behavior in classical systems, often involving colloids.
We use a variety of analytic and numerical approaches.
A complete list of publications can be found on this Google Scholar page. Here is a sample of some key works.
Land-use planning involves complex tradeoffs that reflect the perspectives of multiple stakeholder groups. Physics-based approaches can help identify flashpoint instabilities that are inherent to the process.
Problems in training neural networks are as ubiquitous as the neural networks themselves. Physics-based, sufficient-training algorithms improve generalizability over other leading training approaches.
Technology allows designing and manufacturing objects with unprecedented functionality from emergence. Physics tells us how.
Optimization plays a role in many engineering processes. However it leaves open key engineering questions that are crucial for safety and performance. Physics-based techniques fill the gap.
Engineering hierarchically-structured materials requires breaking natural hierarchies. We show how to do that with pre-assembled building blocks.
Characteristic arrangement patterns determine 'prime real estate' in distributed systems design. But how can patterns be identified?
How do we know if a distributed system design is robust? We show system architectures can be classified by materials-inspired metrics.
Entropy often leads to disorder, but in some circumstances it can promote organization. We determine what those circumstances are.
If we want a material to exhibit a target property, what building blocks do we use to get it? We demonstrate how to do this reverse-engineering in colloidal materials.
To understand chemistry, we think in terms of bonds. But what does a bond require? Does bonding require atoms, electrons, and quantum mechanics?
Entropy is now something we can rationally engineer to make materials organize. How?
Is it possible to design a colloidal material that has a switchablephotonic band gap? We use simulation to show that compressing self-assembled truncated tetrahedra alters structure in a way to shift the band gap.
In designing, e.g., electrical, mechanical, or thermodynamic systems, engineers rely on principles that come from centuries of basic physics investigation. But what are the basic physics principles that guide how to integrate different systems together?
When does matter pack? We find that that for systems of colloids, even when they are found in dense packing structures, they didn't get there by packing.
Solid–solid transitions are ubiquitous in nature and technology, but we still have a lot to learn about them. How can we learn more, and what kind of minimal models can we construct to do so?
How do symmetric, anisotropic objects pack in a spherical container? This simple question is surprisingly difficult to answer, but it has implications for a wide range of physical systems.
Nanoparticle synthesis yields particles that play the role of atoms in nanomaterials, but have properties that can be controlled in ways atoms can't. What does that freedom mean for materials design, and how do we leverage it?
Entropy, especially in the context of anisotropic particle shape, can drive the formation of complex structural order. How does does it do that?
Nanoparticle synthesis inherently yields anisotropically shaped particles. How can we control shape to produce desired bulk behavior?
Here is a selection of non-technical or semi-technical accounts of our work.
Our work on sufficient training of neural networks was described in Queen's Gazette.
Our work on pre-assembly for hierarchical materials was described at MRS Materials 360.
Our work on pre-assembly for hierarchical materials was described at Phys.org.
Our work on when matter packs was described at Phys.org.
Our work on shape driven solid–solid transitions was described at Phys.org.
Our work on packing in confinement was described at Phys.org.
Our work on Digital Alchemy was described in ACS Nano's In Nano.
Our work on shape entropy was described in Nature Materials.
Our work on shape entropy was described at Phys.org.
The wonderful people in our group.
Associate Professor, Physics.
gva@queensu.ca
PhD Candidate, Physics.
hazhir.aliahmadi@queensu.ca
PhD Candidate, Physics.
p.chitnelawong@queensu.ca
