Scientific Papers

The signac framework has been cited over 50 times and has been used in a range of research fields with various types of computational workflows. Here, we showcase a few examples of scientific papers that have used signac. To find additional papers using signac, please refer to citations of the articles mentioned in How to cite signac or this list of citing works on Google Scholar.

We encourage users to add publications citing signac to this list via a pull request. To add a paper, put citation information in signac.bib and follow the formatting of this page. Papers are sorted by publication date, from newest to oldest.


Rose K. Cersonsky, James Antonaglia, Bradley D. Dice, and Sharon C. Glotzer. The diversity of three-dimensional photonic crystals. Nature Communications, 12(1):2543, May 2021. doi:10.1038/s41467-021-22809-6.

Identifies photonic band gaps over a space of 151,593 crystal structures. Simulations performed using MIT Photonic Bands (MPB).


Félix Musil, Max Veit, Alexander Goscinski, Guillaume Fraux, Michael J. Willatt, Markus Stricker, Till Junge, and Michele Ceriotti. Efficient implementation of atom-density representations. The Journal of Chemical Physics, 154(11):114109, March 2021. doi:10.1063/5.0044689.

Benchmarking atom-density representations for use in machine learning. Data generated using librascal.


Eric S. Harper, Eleanor J. Coyle, Jonathan P. Vernon, and Matthew S. Mills. Inverse design of broadband highly reflective metasurfaces using neural networks. Physical Review B, 101(19):195104, May 2020. doi:10.1103/PhysRevB.101.195104.

Electromagnetic simulations of silicon cylinders on a SiO2 substrate are performed using RCWA and managed via signac. The resulting data is used in a machine learning model using TensorFlow to predict optical properties.


Michael P. Howard, Thomas M. Truskett, and Arash Nikoubashman. Cross-stream migration of a Brownian droplet in a polymer solution under Poiseuille flow. Soft Matter, 15(15):3168–3178, March 2019. doi:10.1039/c8sm02552e.

Simulating fluid flow in polymer solutions as a function of chain length \(M\), polymer concentration \(\phi_p\), body force \(f_x\), and droplet viscosity \(\mu_d\). Simulations performed using HOOMD-blue.


Matthew W. Thompson, Ray Matsumoto, Robert L. Sacci, Nicolette C. Sanders, and Peter T. Cummings. Scalable Screening of Soft Matter: A Case Study of Mixtures of Ionic Liquids and Organic Solvents. J. Phys. Chem. B, 123(6):1340–1347, January 2019. doi:10.1021/acs.jpcb.8b11527.

Molecular dynamics simulations of room-temperature ionic liquids spanning 396 state points: 18 compositions by mass fraction and 22 unique solvents from five chemical families (nitriles, alcohols, halocarbons, carbonyls, and glymes). Simulations performed using GROMACS.


Carl S. Adorf, James Antonaglia, Julia Dshemuchadse, and Sharon C. Glotzer. Inverse design of simple pair potentials for the self-assembly of complex structures. The Journal of Chemical Physics, 149(20):204102–204102, November 2018. doi:10.1063/1.5063802.

Uses relative entropy minimization in Fourier space to identify isotropic particle interaction potentials that result in self-assembly of various crystal structures. Simulations performed using HOOMD-blue.


Stephen Thomas, Monet Alberts, Michael M Henry, Carla E Estridge, and Eric Jankowski. Routine million-particle simulations of epoxy curing with dissipative particle dynamics. Journal of Theoretical and Computational Chemistry, 17(03):1840005, May 2018. doi:10.1142/s0219633618400059.

4,480 molecular dynamics simulations of epoxy curing, each containing millions of particles. Simulations performed using HOOMD-blue.


Marco Govoni and Giulia Galli. GW100: comparison of methods and accuracy of results obtained with the WEST code. Journal of Chemical Theory and Computation, 14(4):1895–1909, February 2018. doi:10.1021/acs.jctc.7b00952.

Validation study using five different codes to compute the vertical ionization potential and electron affinities of 100 molecules with the \(G_0W_0\) approximation. Simulations performed using BerkeleyGW, FHI-aims, TURBOMOLE, VASP, and WEST.