(423a) Investigating the Driving Forces of Assembly in Concentrated Electrolyte Solutions

Prakash, A., University of Washington
Pfaendtner, J., University of Washington
Fu, C., University of Washington
Mundy, C. J., Pacific Northwest National Laboratory
The self-assembly of proteins, polymers, and nanoparticles is a complex phenomenon driven by multi-scale, hierarchical interactions with multiple association pathways. All-atom molecular simulations of these systems provide key, molecular-level insights into these processes. Typically, the high computational cost of simulating “realistic” systems is avoided by modeling small assemblies in dilute environments. However, these results provide limited insight and are often hard to corroborate with experiments.

In this talk, I will highlight how recently introduced advanced sampling techniques (metadynamics) can make simulations of crowded, self-assembling systems computationally tractable.1,2 Using this sampling method, we are able to detect of stable clusters of 7 and 13 particle systems with faster convergence than previous metadynamics-based methods. Then, we apply this method to study electrolytes (NaCl and KCl) at different concentrations in solutions and near a surface (ideal mica surface). We find that the free energy of association of electrolytes with other electrolyte atoms, water, and the surface is sensitive to concentration. Since most theories of colloidal association only account for dilute concentration limits, we explore how these microscopic details of association may affect macroscopic association forces.3


(1) Pfaendtner, J.; Bonomi, M. Efficient Sampling of High-Dimensional Free-Energy Landscapes with Parallel Bias Metadynamics. J. Chem. Theory Comput. 2015, 11 (11), 5062–5067.

(2) Prakash, A.; Fu, C. D.; Bonomi, M.; Pfaendtner, J. Biasing Smarter, Not Harder, By Partitioning Collective Variables Into Families. (Under Review). 2018.

(3) Prakash, A.; Pfaendtner, J.; Chun, J.; Mundy, C. J. Quantifying the Molecular-Scale Aqueous Response to the Mica Surface. J. Phys. Chem. C 2017, 121 (34), 18496–18504.