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Water wettability of graphene and graphite, optimization of solid-liquid interaction force fields, and insights from mean-field modeling.

Bladimir Ramos-Alvarado
Published in: The Journal of chemical physics (2019)
A simple mean-field model of carbon-water interactions was developed to predict the binding energy in classical simulations for graphene and graphite surfaces. Using this model, analytical expressions were derived to link microscopic parameters (such as the binding energy) with macroscopic wetting behavior (work of adhesion). Adding these expressions to an optimized mean-field model of wettability, the empirical relationship between the binding energy and the work of adhesion in classical simulations was formally explained. An orientation dependent mean-field model and the insight gained from mean field modeling of the binding energy were used to develop a method to optimize comprehensive carbon-water interaction potentials, where molecular orientation is taken into account using data from state-of-the-art high-resolution multibody electronic structure methods. This method eliminates the ambiguity of finding a set of four parameters by informing on the bounds for the parameter-search process using physics-informed constraints.
Keyphrases
  • high resolution
  • dna binding
  • binding protein
  • single molecule
  • mass spectrometry
  • cystic fibrosis
  • deep learning
  • artificial intelligence
  • cell adhesion
  • tandem mass spectrometry
  • data analysis