Hagan Group

Software & Data

Hagan Group's Github Repository:

https://github.com/hagan-group

 

Recent open source software and data published with our research

Tyukodi B, Hayakawa D, Hall D, Rogers WB, Grason GM, Hagan MF, “Magic sizes enable minimal-complexity, high-fidelity assembly of programmable shells” Phys. Rev. Lett, 135, 118203 (2025),

https://doi.org/10.1103/5yjp-kx2j

Code and Data: https://osf.io/df7ag/

TE Videbæk, D Hayakawa, MF Hagan, GM Grason, S Fraden, WB Rogers, “Measuring
multisubunit mechanics of geometrically-programmed colloidal assemblies via cryo-EM multi-body refinement”, PNAS, 122 (37) e2500716122 (2025),

Paper : https://doi.org/10.1073/pnas.2500716122

Code and Data : 

The cryo-EM data from this study have been deposited in the Electron Microscopy Data
Bank with the following accession codes: EMD-48633, EMD-48634, EMD-48635, EMD-
48636, and EMD-48637. Designs of DNA origami used in this work can be found in the
repository Nanobase and are accessible at: https://nanobase.org/structures/234
and https://nanobase.org/structures/258 . Data associated with this manuscript,
including TEM images for the twisted-corrected tubule, simulation scripts and output,
and cryo-EM analysis scripts and output, have been deposited in a permanent Zenodo
repository and are accessible at: https://doi.org/10.5281/zenodo.15596047 .

LB Frechette, N Sundararajan, F Caballero, A Trubiano, MF Hagan, “Computer simulations show that liquid-liquid phase separation enhances self-assembly”, ACS Nano, 2025, 19, 33, 30275–30291 (2025)

Paper: https://doi.org/10.1021/acsnano.5c08120

Code and Data : 

https://github.com/Layne28/implicit-condensate

https://osf.io/hq2y8/

LB Frechette, A Baskaran, MF Hagan, “Active noise-induced dynamic clustering of passive colloids”, Newton, 1, 100167 (2025)

Paper : https://doi.org/10.1016/j.newton.2025.100167

Code : 

https://github.com/Layne28/ AnalysisTools

https://github.com/Layne28/ ActiveNoise

https://github.com/Layne28/ ActiveNoiseHoomd

https://github.com/Layne28/ active-assembly-hoomd

https://osf.io/7ha8j/

Ghosh S, Baskaran A, Hagan MF, “Achieving designed texture and flows in bulk active nematics using optimal control theory” J. Chem. Phys.162, 134902 (2025)

Paper: https://doi.org/10.1063/5.0244046

Code: https://github.com/ghoshsap/active_nematics_optimalcontrol 

Trubiano, Anthony, and Michael F. Hagan. "Markov State Model Approach to Simulate Self-Assembly." Phys. Rev. X, 14, 041063 (2024)

Paper:  https://doi.org/10.1103/PhysRevX.14.041063

Code:
https://github.com/onehalfatsquared/SAASH

https://github.com/onehalfatsquared/MultiMSM

Economical routes to size-specific assembly of self-closing structures TE Videbæk, D Hayakawa, GM Grason, MF Hagan, S Fraden, WB Rogers Science advances 10 (27), eado5979 (2024)
Paper : https://doi.org/10.1126/sciadv.ado5979 
Data : 
Cryo repository: The cryo-EM data from this study have been deposited in the Electron Microscopy Data Bank with the following accession codes: EMD-43226 and EMD-43227.
 
DNA-origami repository: Designs of DNA origami used in this work can be found in the repository Nanobase and are accessible at https://nanobase.org/structure/234 and https://nanobase.org/structure/235.
Data repository: All TEM images used for this study have been deposited in a permanent Zenodo repository and are accessible at https://doi.org/10.5281/zenodo.10933968.

 

Tran, Phu N., Sattvic Ray, Linnea Lemma, Yunrui Li, Reef Sweeney, Aparna Baskaran, Zvonimir Dogic, Pengyu Hong, and Michael F. Hagan. "Deep-learning optical flow for measuring velocity fields from experimental data." Soft Matter 20, no. 36 (2024): 7246-7257.

Paper: https://doi.org/10.1039/D4SM00483C

Code: https://github.com/tranngocphu/opticalflow-activenematics

Li, Yunrui, Zahra Zarei, Phu N. Tran, Yifei Wang, Aparna Baskaran, Seth Fraden, Michael F. Hagan, and Pengyu Hong. "A machine learning approach to robustly determine director fields and analyze defects in active nematics." Soft Matter 20, no. 8 (2024): 1869-1883.

Paper: https://doi.org/10.1039/D3SM01253K 

Code: https://github.com/siriusxiao62/Orientation-Finder

Ghosh, Saptorshi, Chaitanya Joshi, Aparna Baskaran, and Michael F. Hagan. "Spatiotemporal control of structure and dynamics in a polar active fluid." Soft Matter 20, no. 35 (2024):  7059-7071.

Paper: https://doi.org/10.1039/D4SM00547C

Code: https://github.com/ghoshsap/optimal-control-activepolarfluid

Hagan, Michael F., and Farzaneh Mohajerani. "Self-assembly coupled to liquid-liquid phase separation." PLoS Computational Biology 19, no. 5 (2023): e1010652.

Paper: https://doi.org/10.1371/journal.pcbi.1010652 

Code: https://osf.io/mr9a3/ 

Trubiano, Anthony, and Michael F. Hagan. "Optimization of non-equilibrium self-assembly protocols using Markov state models." The Journal of Chemical Physics 157, no. 24 (2022).

Paper: https://doi.org/10.1063/5.0130407

Code:
https://github.com/onehalfatsquared/CPfold

https://github.com/onehalfatsquared/protocolOptMSM

Joshi, Chaitanya, Sattvic Ray, Linnea M. Lemma, Minu Varghese, Graham Sharp, Zvonimir Dogic, Aparna Baskaran, and Michael F. Hagan. "Data-driven discovery of active nematic hydrodynamics." Physical Review Letters 129, no. 25 (2022): 258001.

Paper: https://doi.org/10.1103/PhysRevLett.129.258001

Code: https://github.com/ joshichaitanya3/actnempy

 Peterson, Matthew SE, Aparna Baskaran, and Michael F. Hagan. "Vesicle shape transformations driven by confined active filaments." Nature Communications 12, no. 1 (2021): 7247.

Paper: https://doi.org/10.1038/s41467-021-27310-8 

Code: https://osf.io/7s9jp/ 

Norton, Michael M., Piyush Grover, Michael F. Hagan, and Seth Fraden. "Optimal control of active nematics." Physical Review Letters 125, no. 17 (2020): 178005.

Paper: https://doi.org/10.1103/PhysRevLett.125.178005 

Code: https://osf.io/qyk9t/ 

Duclos, Guillaume, Raymond Adkins, Debarghya Banerjee, Matthew SE Peterson, Minu Varghese, Itamar Kolvin, Arvind Baskaran et al. "Topological structure and dynamics of three-dimensional active nematics." Science 367, no. 6482 (2020): 1120-1124.

Paper: https://doi.org/10.1126/science.aaz4547 

Code: https://datadryad.org/stash/dataset/doi:10.25349/D9CS31