(120j) Enhanced Sampling and Inverse Design of Self-Assembling Patchy and Polarizable Colloids | AIChE

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(120j) Enhanced Sampling and Inverse Design of Self-Assembling Patchy and Polarizable Colloids

Authors 

Dasetty, S. - Presenter, Clemson University
De Pablo, J. - Presenter, University of Wisconsin-Madison
Dhanasekaran, J., The University of Chicago
Li, J., University of Chicago
Ferguson, A., University of Chicago
Understanding self-assembly of colloids is of both fundamental interest and integral to rational design of building blocks of engineered materials with desired properties [1]. The integration of machine learning and in silico modeling has enabled high-throughput virtual screening and data-driven inverse materials design for colloidal systems [1-6]. We recently reported the landscape engineering approach and applied it to the inverse design of patchy colloids to self-assemble into colloidal crystals with omnidirectional photonic band gaps [2]. In this work, we build upon these foundations to also incorporate the effects of charge and polarizability in many-body colloidal assembly. This expands the design space of the colloidal building blocks, advances the experimental realism of the simulations, and opens up possibilities for directed assembly through the application and control of external fields. To do so, we have employed the recently developed image method [7] to capture many-body polarization effects via multiple-scattering formalism [8, 9] for spherical colloidal particles and applied collective variable discovery and enhanced sampling to efficiently map out and optimize the free energy landscapes governing assembly [2, 11]. We apply these tools to the self-assembly of patchy, polarizable, spherical colloids to map out their phase diagrams and reverse engineer desired structures. We also discuss preliminary extensions of our approach to non-spherical particles using a scalable boundary element formalism [10] and report the incorporation of our tools in open source simulation and enhanced sampling codes [11, 12].

References

[1] Sherman ZM, Howard MP, Lindquist BA, Jadrich RB, Truskett TM. Inverse methods for design of soft materials. The Journal of Chemical Physics. 2020 Apr 14;152(14):140902.
[2] Ferguson AL. Machine learning and data science in soft materials engineering. Journal of Physics: Condensed Matter. 2017 Dec 22;30(4):043002.

[3] Lindquist BA, Jadrich RB, Truskett TM. Communication: Inverse design for self-assembly via on-the-fly optimization.
[4] Long AW, Phillips CL, Jankowksi E, Ferguson AL. Nonlinear machine learning and design of reconfigurable digital colloids. Soft Matter. 2016;12(34):7119-35.

[5] Long AW, Ferguson AL. Rational design of patchy colloids via landscape engineering. Molecular Systems Design & Engineering. 2018;3(1):49-65.
[6] Ma Y, Ferguson AL. Inverse design of self-assembling colloidal crystals with omnidirectional photonic bandgaps. Soft matter. 2019;15(43):8808-26.

[7] Qin J, de Pablo JJ, Freed KF. Image method for induced surface charge from many-body system of dielectric spheres. The Journal of chemical physics. 2016 Sep 28;145(12):124903. [8] Qin J, Li J, Lee V, Jaeger H, de Pablo JJ, Freed KF. A theory of interactions between polarizable dielectric spheres. Journal of colloid and interface science. 2016 May 1;469:237- 41.

[9] Qin J. Charge polarization near dielectric interfaces and the multiple-scattering formalism. Soft matter. 2019;15(10):2125-34.
[10] Jiang X, Li J, Zhao X, Qin J, Karpeev D, Hernandez-Ortiz J, de Pablo JJ, Heinonen O. An O (N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles. The Journal of Chemical Physics. 2016 Aug 14;145(6):064307.

[11] Sidky H, Colón YJ, Helfferich J, Sikora BJ, Bezik C, Chu W, Giberti F, Guo AZ, Jiang X, Lequieu J, Li J. SSAGES: software suite for advanced general ensemble simulations. The Journal of chemical physics. 2018 Jan 28;148(4):044104.[12] Plimpton S. Fast parallel algorithms for short-range molecular dynamics. Sandia National Labs., Albuquerque, NM (United States); 1993 May 1.

[12] Plimpton S. Fast parallel algorithms for short-range molecular dynamics. Sandia National Labs., Albuquerque, NM (United States); 1993 May 1.

Topics 

Computational Molecular Engineering
Materials

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