Work Description

Inverse design of triblock Janus spheres for self-assembly of complex structures in the crystallization slot via digital alchemy

Luis Y. Rivera-Rivera, Timothy C. Moore & Sharon C. Glotzer

Method

The digital alchemy framework is an extended ensemble simulation technique that incorporates particle attributes as thermodynamic variables, enabling the inverse design of colloidal particles for desired behavior. Here, we extend the digital alchemy framework for the inverse design of patchy spheres that self-assemble into target crystal structures. To constrain the potentials to non-trivial solutions, we conduct digital alchemy simulations with constant second virial coefficient. We optimize the size, range, and strength of patchy interactions in model triblock Janus spheres to self-assemble the 2D kagome and snub square lattices and the 3D pyrochlore lattice, and demonstrate self-assembly of all three target structures with the designed models.

Alchemical and self-assembly simulations were performed in the HOOMD-blue simulation engine. Simulation trajectories containing particle configurations (positions and orientations) were generated in the gsd format. Structure characterization of the self-assembly simulations (yield and radial distribution functions) were computed with the freud analysis package. All the simulation snapshots were rendered with fresnel, except Figure 5a which was rendered in ovito. Data state points were generated and managed with the signac data management tool.

Relevant documentation:

• HOOMD-blue - https://hoomd-blue.readthedocs.io/en/v4.0.0/
• signac - https://docs.signac.io/en/latest/
• freud - https://freud.readthedocs.io/en/latest/index.html
• ovito - https://www.ovito.org/about/
• gsd - https://gsd.readthedocs.io/en/stable/python-api.html
• fresnel - https://fresnel.readthedocs.io/en/v0.13.5/
• numpy - https://numpy.org/doc/stable/user/index.html

References:

• J. A. Anderson, J. Glaser, and S. C. Glotzer. HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations. Computational Materials Science 173: 109363, Feb 2020. 10.1016/j.commatsci.2019.109363
• Carl S. Adorf, Paul M. Dodd, Vyas Ramasubramani, and Sharon C. Glotzer. Simple data and workflow management with the signac framework. Comput. Mater. Sci., 146(C):220–229, 2018. doi:10.1016/j.commatsci.2018.01.035.
• Vyas Ramasubramani, Carl S. Adorf, Paul M. Dodd, Bradley D. Dice, and Sharon C. Glotzer. signac: A Python framework for data and workflow management. In Proceedings of the 17th Python in Science Conference, 152–159. 2018. doi:10.25080/Majora-4af1f417-016.
• V. Ramasubramani, B. D. Dice, E. S. Harper, M. P. Spellings, J. A. Anderson, and S. C. Glotzer. freud: A Software Suite for High Throughput Analysis of Particle Simulation Data. Computer Physics Communications Volume 254, September 2020, 107275. doi:10.1016/j.cpc.2020.107275.
• A. Stukowski. Visualization and analysis of atomistic simulation data with OVITO – the Open Visualization Tool, Modelling Simul. Mater. Sci. Eng. 18 (2010), 015012. doi:10.1088/0965-0393/18/1/015012

Description

This dataset was generated for our work "Inverse design of triblock Janus spheres for self-assembly of complex structures in the crystallization slot via digital alchemy", published in Soft Matter vol 19, 2023 (doi: https://doi.org/10.1039/d2sm01593e) The dataset is organized as follows: the data for each of the three target structures is contained within a directory with the structure name (e.g., kagome, pyrocholore and snub-square). Within each structure directory, data obtained from alchemical and self-assembly simulations are separated into alchem and self-assembly directories respectively. An additional suboptimal-self-assembly directory is only present for the snub-square structure and contains the data for the pattern registration analysis discussed in the SI. Below is list of all the files contained within each directory:

1. alchem directory:

- S_alpha_mc_sweeps_X.txt - evolution of the alchemical variables as function of MC sweeps, where S is the structure and X is simply an integer specifying a different initial condition
- optimal_alpha_b2_phi_Y.txt - optimal alchemical variables as function of second virial coefficient, where Y is specifies the target crystal density

1. self-assembly directory:

   • anneal.gsd - simulation trajectory of the annealing step from high to target temperature
   • equilibrate.gsd - simulation trajectory at constant temperature
   • anneal_env_match.npy - number of particles whose environment matches that of the target structure during the annealing step
   • equilibrate_env_match.npy - number of particles whose environment matches that of the target structure during the equilibration step
   • potential_energy.txt - system's potential energy as function of MC sweeps, contain combined energy for both annealing and equilibration steps
   • rdf_phi_X.txt - radial distribution function of the assembled structure with optimal alchemical variables, where X is the target crystal density
   • traj.gsd - combined trajectory for anneal + equilibration steps, only present for the snub-square structure
   • env_match.npy - number of particles whose environment matches that of the target structure for the anneal + equilibration steps, only present for the snub-square structure

2. suboptimal-self-assembly directory

   • theta_L_Xdeg.gsd - self-assembly trajectory for asymmetric triblock Janus particles, where X denotes the aperture angle of the larger patch while the smaller patch is fixed at the optimal value obtained from alchemical simulations ( ~ 38 degrees)

Note: .npy is the the standard binary file format in NumPy