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Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck Based Enhanced Sampling

Journal Article · · Journal of Physical Chemistry. B
 [1];  [1];  [2]
  1. Univ. of Maryland, College Park, MD (United States)
  2. Univ. of Maryland, College Park, MD (United States); Univ. of Maryland, Bethesda, MD (United States). Institute for Health Computing
Here, 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.
Research Organization:
Univ. of Maryland, College Park, MD (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
SC0021009
OSTI ID:
3003392
Alternate ID(s):
OSTI ID: 2577478
Journal Information:
Journal of Physical Chemistry. B, Journal Name: Journal of Physical Chemistry. B Journal Issue: 34 Vol. 128; ISSN 1520-6106; ISSN 1520-5207
Publisher:
American Chemical SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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