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Published on: August 14, 2018
Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck Based Enhanced
Vanessa J Meraz1, Ziyue Zou2, Pratyush Tiwary1,2,3
1Institute for Physical Science and Technology, University of Maryland, College Park, Maryland 20742, United States.
Abstract:
We investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here, with particles interacting through a repulsive screened Coulomb Yukawa potential, proceeds from vapor to dense liquid droplet to crystalline phases across multiple high barriers. Employing a one-dimensional reaction coordinate derived from the State Predictive Information Bottleneck framework, our simulations capture back-and-forth phase transitions across multiple barriers effectively in biased metadynamics simulations. We obtain relative free energy differences between different phases and also quantify the roles of different molecular level features in driving the phase changes.
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