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Energy Renormalization for Coarse-Graining the Dynamics of a Model Glass-Forming Liquid. | LitMetric

AI Article Synopsis

  • Coarse-grained modeling improves efficiency for simulating glass-forming materials by simplifying molecular interactions, but effective methods for capturing dynamics are lacking.
  • Using an energy-renormalization approach combined with a localization model, researchers demonstrate how to maintain key dynamic properties during coarse-graining.
  • Their work on ortho-terphenyl shows that this method can accurately predict both short- and long-term dynamics across all temperatures in glass formation, paving the way for better predictive models in material science.

Article Abstract

Coarse-grained modeling achieves the enhanced computational efficiency required to model glass-forming materials by integrating out "unessential" molecular degrees of freedom, but no effective temperature transferable coarse-graining method currently exists to capture dynamics. We address this fundamental problem through an energy-renormalization scheme, in conjunction with the localization model of relaxation relating the Debye-Waller factor ⟨u⟩ to the structural relaxation time τ. Taking ortho-terphenyl as a model small-molecule glass-forming liquid, we show that preserving ⟨u⟩ (at picosecond time scale) under coarse-graining by renormalizing the cohesive interaction strength allows for quantitative prediction of both short- and long-time dynamics covering the entire temperature range of glass formation. Our findings provide physical insights into the dynamics of cooled liquids and make progress for building temperature-transferable coarse-grained models that predict key properties of glass-forming materials.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6217959PMC
http://dx.doi.org/10.1021/acs.jpcb.8b00321DOI Listing

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